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 .
Response to Amendment
The Amendment filed on July 23, 2026, has been entered. Claims 1, 4, 6-9, 11-12, 15, and 17-20 are amended. Claims 2, 5, 10, 13, and 16 are cancelled. Claims 1, 3-4, 6-9, 11-12, 14-15, and 17-20 remain pending in the application.
Response to Arguments
Claim Rejections under 35 U.S.C. 101
The rejection of claims 1, 3-4, 6-9, 11-12, 14-15, and 17-20 (as amended) under 35 U.S.C. 101 has been maintained.
Applicant’s arguments (filed 07/23/2026) have been fully considered but have been found unpersuasive.
From applicant’s arguments, the Applicant disagrees in view of the amendments (in pgs. 8-11 of Applicant’s remarks) that claim 1 is not directed to an abstract idea, but to a specific technological technique for improving machine-learning model training and intent inferencing systems.
Examiner respectfully disagrees with Applicant’s argument above and did not find the applicant’s arguments to be persuasive. In regard to encoding customer-provided data as an intent embedding this step can be done mentally a person can mentally evaluate an intent from a provided list of data and write a code as a vector representation considered as an intent embedding, this is the broadest reasonable interpretation of encoding as applicant’s specification does not further define encoding in this context. Comparing the intent embedding with item embeddings of a training workspace to generate a similarity score can also be done mentally a person can mentally evaluate the vector representation for the intent from the data to other vector representations and do a mathematical formula to compute a distance/score of how similar the vectors representation of the first intent is to other vector representations of a training dataset by use of pen and paper. For determining whether the similarity score exceeds a threshold a person can mentally determine whether an already existing item has a vector representation that corresponds to a computed similarity score that is greater than a given threshold in response to the computed similarity score being greater than a given threshold. Mapping the first intent to an existing item responsive to the similarity score exceeding the threshold can also be done mentally a person can mentally evaluate matching/mapping the vector representation for the intent from the data to an existing vector representation which can be linked to an item and incrementing a count by one in a table or matrix that can be created using pen and paper. Incrementing a corresponding count associated with the existing item can also be done via mind and use of pen and paper. Creating a matrix comprising corresponding counts of one or more items associated with each of a plurality of training workspaces can also be done via mind and use of pen and paper a table can be created acting as a matrix and using training workspaces to fill in said table, applicant’s specification did not further define a matrix and the broadest reasonable interpretation of a matrix is applied.
The Applicant further disagrees in view of the amendments (in pg. 11 of Applicant’s remarks) that the amended claim integrates any such alleged abstract idea into a practical application in Step 2A Prong Two.
Examiner respectfully still disagrees with Applicant’s argument above and did not find the applicant’s arguments to be persuasive. Applicant did not elaborate further or show an improvement on the training technique for using a created matrix to train models, this is mere instructions to apply the judicial exception, training machine learning model(s) using the created matrix is mere instructions to apply the judicial exception, the applicant does not elaborate further or show an improvement on the training technique for using a matrix to train models. Deploying a trained second machine-learning model and performing inferencing based on the deployed second machine-learning model is also still mere instructions to apply a judicial exception, applicant argues that it is not limited to generating a machine-learning model, but further applies the trained model in an operational computing environment to automatically perform intent inferencing based on customer-provided data, however this is still just mere instructions as deploying a machine-learning model and using it to generate a recommendation/perform inferencing is a known function and process in the art. Additionally, generating data is a well-known conventional activity in the context of generating recommendations.
The Applicant further disagrees in view of the amendments (in pgs. 11-12 of Applicant’s remarks) that because they concluded at Step 2A Prong One/Two, the patent-eligibility analysis does not proceed to Step 2B, therefore, amended independent claim 1 satisfies the requirements of Step 2B and is directed to patent-eligible subject matter in Step 2B.
Examiner respectfully disagrees with Applicant’s argument above and did not find the applicant’s arguments to be persuasive. Examiner disagrees that Step 2A Prong One/Two, the patent-eligibility analysis does not proceed to Step 2B in previous limitations and the additional limitations addressed in Step 2A Prong Two does not amount to significantly more. As stated previously, applicant did not elaborate further or show an improvement on the training technique for using a created matrix to train models, this is mere instructions to apply the judicial exception, training machine learning model(s) using the created matrix is mere instructions to apply the judicial exception, the applicant does not elaborate further or show an improvement on the training technique for using a matrix to train models. Deploying a trained second machine-learning model and performing inferencing based on the deployed second machine-learning model is also still mere instructions to apply a judicial exception, applicant argues that it is not limited to generating a machine-learning model, but further applies the trained model in an operational computing environment to automatically perform intent inferencing based on customer-provided data, however this is still just mere instructions as deploying a machine-learning model and using it to generate a recommendation/perform inferencing is a known function and process in the art. Additionally, generating data is a well-known conventional activity in the context of generating recommendations. All of these limitations do not amount to significantly more than the judicial exception and as such still fails to satisfy the requirements of Step 2B. Please see examiner’s rejections under 35 U.S.C. 101.
Claim Rejections under 35 U.S.C. 103
The rejection of claims 1, 3-4, 11-12, and 14-15 (as amended) under 35 U.S.C. 103 has been maintained.
The rejection of claims 6 and 17 (as amended) under 35 U.S.C. 103 has been maintained.
The rejection of claims 7-9, and 18-20 (as amended) under 35 U.SC. 103 has been maintained.
Regarding the applicant’s arguments on rejections under 35 U.S.C 103, Applicant’s arguments with respect to claim(s) 1, 3-4, 6-9, 11-12, 14-15, and 17-20 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument. Please see examiner’s rejections under 35 U.S.C. 103.
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 rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea (mental process) without significantly more.
Claim 1:
Regarding claim 1, in step 1 of the 101-analysis set forth in MPEP 2106, the claim recites “A method comprising: encoding, using at least one hardware processor…”, and a method is a process which is one of the four statutory categories of invention.
In step 2A prong 1 of the 101-analysis set forth in the MPEP 2106, the examiner has determined that the following limitations recite a process that, under the broadest reasonable interpretation, covers a mental process but for recitation of generic computer components:
“encoding…, a first intent from customer provided data as an intent embedding” (this is a mental process, a person can mentally evaluate an intent from provided list of data and write a code as a vector representation considered as an intent embedding, see MPEP § 2106.04(a)(2)(III)),
“comparing…, the intent embedding of the first intent and an intent embedding corresponding to one or more items of a training workspace to generate a similarity score” (this is a mental process, a person can mentally evaluate the vector representation for the intent from the data to other vector representations and do a mathematical formula to compute a distance/score of how similar the vectors representation of the first intent is to other vector representations of a training dataset by use of pen and paper, see MPEP § 2106.04(a)(2)(III)),
“determining whether an existing item of the one or more items has an item embedding corresponding to the similarity score greater than a given threshold;” (this is a mental process, a person can mentally determine whether an already existing item has a vector representation that corresponds to a computed similarity score that is greater than a given threshold in response to the computed similarity score being greater than a given threshold, see MPEP § 2106.04(a)(2)(III)),
“in response to determining that the similarity score is greater than the given threshold, mapping…, the first intent to the existing item and incrementing a corresponding count of the existing item in the training workspace by one“ (this is a mental process, a person can mentally evaluate matching/mapping the vector representation for the intent from the data to an existing vector representation which can be linked to an item and incrementing a count by one in response to a determination of a similarity score being greater than a threshold, see MPEP § 2106.04(a)(2)(III)),
“creating…, a matrix comprising corresponding counts of the one or more items associated with each of a plurality of training workspaces, wherein the plurality of training workspaces comprises the training workspace” (this is a mental process, a person can mentally evaluate creating a matrix that comprises corresponding counts of training datasets, see MPEP § 2106.04(a)(2)(III)),
If claim limitations, under the broadest reasonable interpretation, covers performance of the limitations as a mental process but for the recitation of generic computer components, then it falls within the mental process grouping of abstract ideas. Accordingly, the claim “recites” an abstract idea.
In step 2A prong 2 of the 101-analysios set forth in MPEP 2106, the examiner has determined that the following additional elements do not integrate this judicial exception into a practical application:
“using at least one hardware processor” (Using processors is considered generic computer component being used as tool to perform functions of the judicial exception – see MPEP § 2106.05(f)),
“training, using the at least one hardware processor, at least a first machine learning model using one or more of the plurality of training workspaces included in the created matrix” (training a machine learning model is considered mere instructions to apply an exception using generic computer – see MPEP § 2106.05(f)),
“training, using the at least one hardware processor, at least a second machine learning model using the created matrix” (As previously mentioned, training a machine learning model is considered mere instructions to apply an exception using generic computer – see MPEP § 2106.05(f)),
“deploying, using the at least one hardware processor, the at least second machine learning model;” (deploying of a machine learning model is considered mere instructions to apply an exception using generic computer – see MPEP § 2106.05(f)),
“and performing, using the at least one hardware processor, inferencing based on the deployed at least second machine learning model.” (performing inferencing based on a machine learning model is considered mere instructions to apply an exception using generic computer – see MPEP § 2106.05(f))
Since the claim as a whole, looking at the additional elements individually and in combination, does not contain any other additional elements that are indicative of integration into a practical application, the claim is “directed” to an abstract idea.
In step 2B of the 101-analysis set forth in the 2019 PEG, the examiner has determined that the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
As discussed above, additional element vi recites a generic computer component being used as tool to perform functions of the judicial exception, and additional elements vii, viii, ix, and x recite mere instructions to apply the judicial exception using generic computer components, which are not indicative of significantly more.
Considering the additional elements individually and in combination, and the claim as a whole, the additional elements do not provide significantly more than the abstract idea. Therefore, the claim is not patent eligible.
Claim 3:Regarding claim 3, it is dependent upon claim 1, and thereby incorporates the limitations of, and corresponding analysis applied to claim 1. Further claim 3 recites the following abstract idea:
“The method of claim 1, further comprising repeating the comparing operation and creating a new item corresponding to a second intent in response to the similarity score being less than the given threshold and setting a count corresponding to the new item to one” (this is considered a mental process, since a person can mentally evaluate and repeat the comparing limitation from claim 1 to identify a new item based on the chosen mathematical formula to compute a score being less than the threshold of an already established item and incrementing the count for this new item by use of pen and paper, see MPEP § 2106.04(a)(2)(III)),
If claim limitations, under their broadest reasonable interpretation, covers performance of the limitations as a mental process but for the recitation of generic compute components, then it falls within the mental process grouping of abstract ideas. Accordingly, the claim “recites” an abstract idea.
Since the claim does not recite additional elements that either integrate the judicial exception into a practical application, nor provide significantly more than the judicial exception, the claim is not patent eligible.
Claim 4:
Regarding claim 4, it is dependent upon claim 3, and thereby incorporates the limitations of, and corresponding analysis applied to claim 3. Further claim 4 recites the following abstract ideas:
“The method of claim 3, further comprising: clustering client log data into candidate intents and mapping the candidate intents to the one or more items of the matrix” (this is considered a mental process, since a person can mentally evaluate clustering client log data to candidate intents and mapping/matching these intents to items of the matrix established in claim 1, see MPEP § 2106.04(a)(2)(III)),
“creating at least one intent recommendation based on intents corresponding to the one or more items of the training workspace” (this is considered a mental process, since a person can mentally evaluate identifying and creating a recommendation based on intents in correspondence to items from training dataset, see MPEP § 2106.04(a)(2)(III)),
If claim limitations, under their broadest reasonable interpretation, covers performance of the limitations as a mental process but for the recitation of generic compute components, then it falls within the mental process grouping of abstract ideas. Accordingly, the claim “recites” an abstract idea.
Since the claim does not recite additional elements that either integrate the judicial exception into a practical application, nor provide significantly more than the judicial exception, the claim is not patent eligible.
Claim 6:
Regarding claim 6, it is dependent upon claim 3, and thereby incorporates the limitations of, and corresponding analysis applied to claim 3. Further claim 6 recites the following abstract idea:
“The method of claim 3, further comprising repeating the encoding, comparing, mapping, creating the new item, and creating the matrix operations, for each workspace to update the matrix with each pair of training workspace and item” (this is considered a mental process, since a person can mentally evaluate using the encoding, comparing, mapping, creation of a new item, and creation of the matrix limitations from claim 1, to then make updates to the created matrix with each training dataset and item, see MPEP § 2106.04(a)(2)(III)),
If claim limitations, under their broadest reasonable interpretation, covers performance of the limitations as a mental process but for the recitation of generic compute components, then it falls within the mental process grouping of abstract ideas. Accordingly, the claim “recites” an abstract idea.
Since the claim does not recite additional elements that either integrate the judicial exception into a practical application, nor provide significantly more than the judicial exception, the claim is not patent eligible.
Claim 7:
Regarding claim 7, it is dependent upon claim 3, and thereby incorporates the limitations of, and corresponding analysis applied to claim 3. Further claim 7 recites the following abstract ideas:
“The method of claim 3, further comprising: mapping a given client workspace into the matrix” (this is considered a mental process, since a person can mentally evaluate mapping/matching a client dataset into a matrix, see MPEP § 2106.04(a)(2)(III)),
“grouping utterances of client log data into clusters and mapping the clusters into items of the similar training workspace using the corresponding intent embeddings” (this is considered a mental process, since a person can mentally evaluate grouping utterances of client log data into clusters/groupings and mapping/matching these groupings/clusters into items of the similar training dataset using the corresponding vector representations considered as embeddings, see MPEP § 2106.04(a)(2)(III)),
“recommending the first and second intents corresponding to the mapped items that exist in the similar training workspace and are absent from the given client workspace” (this is considered a mental process, since a person can mentally evaluate and recommend a first and second intent in correspondence to mapped/matched items that exist from the similar training dataset and that are missing from the client dataset, see MPEP § 2106.04(a)(2)(III)),
If claim limitations, under their broadest reasonable interpretation, covers performance of the limitations as a mental process but for the recitation of generic compute components, then it falls within the mental process grouping of abstract ideas. Accordingly, the claim “recites” an abstract idea.
Further, claim 7 recites the following additional element:
“conducting a search of the training workspace that contains a similar set of items compared to the given client workspace” (In step 2A, prong 2, this is considered mere instructions to apply an exception using generic computer with the generate task-specific output operation performed by any generic computer, see MPEP § 2106.05(f)). (In step 2B, this is also considered mere instructions to apply an exception using generic computer - see MPEP § 2106.05(f)).
Since the claim does not recite additional elements that either integrate the judicial exception into a practical application, nor provide significantly more than the judicial exception, the claim is not patent eligible.
Claim 8:
Regarding claim 8, it is dependent upon claim 3, and thereby incorporates the limitations of, and corresponding analysis applied to claim 3. Further claim 8 recites the following abstract ideas:
“The method of claim 3, further comprising: mapping a given client workspace into the matrix” (this is considered a mental process, since a person can mentally evaluate mapping/matching a client dataset into a matrix, see MPEP § 2106.04(a)(2)(III)),
“recommending the first and second intents corresponding to the mapped items that exist in the similar training workspace and are absent from the given client workspace” (this is considered a mental process, since a person can mentally evaluate recommending first and second intents in correspondence to mapped/matched items that exist from the similar training dataset and that are missing from the client dataset, see MPEP § 2106.04(a)(2)(III)),
If claim limitations, under their broadest reasonable interpretation, covers performance of the limitations as a mental process but for the recitation of generic compute components, then it falls within the mental process grouping of abstract ideas. Accordingly, the claim “recites” an abstract idea.
Further, claim 8 recites the following additional element:
“conducting a search of the training workspace(s) that contain a similar set of items in the matrix compared to the given client workspace” (In step 2A, prong 2, this is considered mere instructions to apply an exception using generic computer with the generate task-specific output operation performed by any generic computer, see MPEP § 2106.05(f)). (In step 2B, this is also considered mere instructions to apply an exception using generic computer - see MPEP § 2106.05(f)).
Since the claim does not recite additional elements that either integrate the judicial exception into a practical application, nor provide significantly more than the judicial exception, the claim is not patent eligible.
Claim 9:
Regarding claim 9, it is dependent upon claim 3, and thereby incorporates the limitations of, and corresponding analysis applied to claim 3. Further claim 9 recites the following abstract ideas:
“The method of claim 3, further comprising: grouping utterances of client log data into clusters and mapping the clusters into items of the training workspace using the corresponding intent embeddings” (this is considered a mental process, since a person can mentally evaluate grouping utterances of client log data into clusters/groupings and mapping/matching these groupings/clusters into items of the training datasets using the corresponding vector representations considered as embeddings, see MPEP § 2106.04(a)(2)(III)),
“recommending the first and second intents corresponding to the mapped items that exist in the training workspace” (this is considered a mental process, since a person can mentally evaluate recommending first and second intents in correspondence to mapped/matched items that exist from the training dataset, see MPEP § 2106.04(a)(2)(III)),
If claim limitations, under their broadest reasonable interpretation, covers performance of the limitations as a mental process but for the recitation of generic compute components, then it falls within the mental process grouping of abstract ideas. Accordingly, the claim “recites” an abstract idea.
Since the claim does not recite additional elements that either integrate the judicial exception into a practical application, nor provide significantly more than the judicial exception, the claim is not patent eligible.
Claim 11:
Regarding claim 11, in step 1 of the 101-analysis set forth in MPEP 2106, the claim recites “A computer program product, comprising: one or more tangible computer-readable storage media and program instructions stored on at least one of the one or more tangible computer-readable storage media, the program instructions executable by a processor, the program instructions…”, and a computer program product is a machine which is one of the four statutory categories of invention.
In step 2A prong 1 of the 101-analysis set forth in the MPEP 2106, the examiner has determined that the following limitations recite a process that, under the broadest reasonable interpretation, covers a mental process but for recitation of generic computer components:
“encoding, a first intent from customer provided data as an intent embedding” (this is a mental process, a person can mentally evaluate an intent from provided list of data and write a code as a vector representation considered as an intent embedding, see MPEP § 2106.04(a)(2)(III)),
“comparing, the intent embedding of the first intent and an intent embedding corresponding to one or more items of a training workspace to generate a similarity score” (this is a mental process, a person can mentally evaluate the vector representation for the intent from the data to other vector representations and do a mathematical formula to compute a distance/score of how similar the vectors representation of the first intent is to other vector representations of a training dataset by use of pen and paper, see MPEP § 2106.04(a)(2)(III)),
“determining whether an existing item of the one or more items has an item embedding corresponding to the similarity score greater than a given threshold;,” (this is a mental process, a person can mentally determine whether an already existing item has a vector representation that corresponds to a computed similarity score that is greater than a given threshold in response to the computed similarity score being greater than a given threshold, see MPEP § 2106.04(a)(2)(III)),
“in response to determining that the similarity score is greater than the given threshold, mapping, the first intent to the existing item and incrementing a corresponding count of the existing item in the training workspace by one;” (this is a mental process, a person can mentally evaluate matching/mapping the vector representation for the intent from the data to an existing item and incrementing the count of this item in response to a determination of a similarity score being greater than a threshold, see MPEP § 2106.04(a)(2)(III)),
“creating a matrix, the created matrix comprising corresponding counts of the one or more items associated with each of a plurality of training workspaces, wherein the plurality of training workspaces comprises the training workspace;” (this is a mental process, a person can mentally evaluate creating a matrix that comprises corresponding counts of training datasets, see MPEP § 2106.04(a)(2)(III)),
If claim limitations, under the broadest reasonable interpretation, covers performance of the limitations as a mental process but for the recitation of generic computer components, then it falls within the mental process grouping of abstract ideas. Accordingly, the claim “recites” an abstract idea.
In step 2A prong 2 of the 101-analysios set forth in MPEP 2106, the examiner has determined that the following additional elements do not integrate this judicial exception into a practical application:
“A computer program product, comprising: one or more tangible computer-readable storage media and program instructions stored on at least one of the one or more tangible computer-readable storage media, the program instructions executable by a processor” (Using processors and a tangible computer-readable storage media with program instructions stored on it is considered mere instructions to apply an exception using generic computer – see MPEP § 2106.05(f)),
“training at least a first machine learning model using one or more of the plurality of training workspaces included in the created matrix” (training a machine learning model is considered mere instructions to apply an exception using generic computer – see MPEP § 2106.05(f)),
“training at least a second machine learning model using the created matrix” (As previously mentioned, training a machine learning model and using a processor is considered mere instructions to apply an exception using generic computer – see MPEP § 2106.05(f)),
“deploying the at least second machine learning model for performing inferencing.” (facilitating deployment of a machine learning model is considered mere instructions to apply an exception using generic computer – see MPEP § 2106.05(f)),
“and performing the inferencing based on the deployed at least second machine learning model.” (performing inferencing based on a machine learning model is considered mere instructions to apply an exception using generic computer – see MPEP § 2106.05(f))
Since the claim as a whole, looking at the additional elements individually and in combination, does not contain any other additional elements that are indicative of integration into a practical application, the claim is “directed” to an abstract idea.
In step 2B of the 101-analysis set forth in the 2019 PEG, the examiner has determined that the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
As discussed above, additional elements vi, vii, viii, ix, and x recite mere instructions to apply the judicial exception using generic computer components, which are not indicative of significantly more.
Considering the additional elements individually and in combination, and the claim as a whole, the additional elements do not provide significantly more than the abstract idea. Therefore, the claim is not patent eligible.
Claim 12:
Regarding claim 12, in step 1 of the 101-analysis set forth in MPEP 2106, the claim recites “A system comprising: a memory; and at least one processor, coupled to said memory, and operative to perform operations…”, and a system in this instance is a machine which is one of the four statutory categories of invention.
In step 2A prong 1 of the 101-analysis set forth in the MPEP 2106, the examiner has determined that the following limitations recite a process that, under the broadest reasonable interpretation, covers a mental process but for recitation of generic computer components:
“encoding, a first intent from customer provided data as an intent embedding” (this is a mental process, a person can mentally evaluate an intent from provided list of data and write a code as a vector representation considered as an intent embedding, see MPEP § 2106.04(a)(2)(III)),
“comparing, the intent embedding of the first intent and an intent embedding corresponding to one or more items of a training workspace to generate a similarity score” (this is a mental process, a person can mentally evaluate the vector representation for the intent from the data to other vector representations and do a mathematical formula to compute a distance/score of how similar the vectors representation of the first intent is to other vector representations of a training dataset by use of pen and paper, see MPEP § 2106.04(a)(2)(III)),
“determining whether an existing item of the one or more items has an item embedding corresponding to the similarity score greater than a given threshold; (this is a mental process, a person can mentally determine whether an already existing item has a vector representation that corresponds to a computed similarity score that is greater than a given threshold in response to the computed similarity score being greater than a given threshold, see MPEP § 2106.04(a)(2)(III)),
“in response to determining that the similarity score is greater than the given threshold, mapping, the first intent to the existing item and incrementing a corresponding count of the existing item in the training workspace by one;” (this is a mental process, a person can mentally evaluate matching/mapping the vector representation for the intent from the data to an existing item and incrementing the count of this item, see MPEP § 2106.04(a)(2)(III)),
“creating a matrix, the created matrix comprising corresponding counts of the one or more items associated with each of a plurality of training workspaces, wherein the plurality of training workspaces comprises the training workspace;” (this is a mental process, a person can mentally evaluate creating a matrix that comprises corresponding counts of training datasets, see MPEP § 2106.04(a)(2)(III)),
If claim limitations, under the broadest reasonable interpretation, covers performance of the limitations as a mental process but for the recitation of generic computer components, then it falls within the mental process grouping of abstract ideas. Accordingly, the claim “recites” an abstract idea.
In step 2A prong 2 of the 101-analysios set forth in MPEP 2106, the examiner has determined that the following additional elements do not integrate this judicial exception into a practical application:
“A system comprising: a memory; and at least one processor, coupled to said memory, and operative to perform operations” (Using a processor and memory is considered a generic computer component being used as tool to perform functions of the judicial exception – see MPEP § 2106.05(f)),
“training at least a first machine learning model using one or more of the plurality of training workspaces included in the created matrix” (training a machine learning model is considered mere instructions to apply an exception using generic computer – see MPEP § 2106.05(f)),
“training at least a second machine learning model using the created matrix” (As previously mentioned, training a machine learning model and using a processor is considered mere instructions to apply an exception using generic computer – see MPEP § 2106.05(f)),
“deploying the at least second machine learning model for performing inferencing.” (facilitating deployment of a machine learning model is considered mere instructions to apply an exception using generic computer – see MPEP § 2106.05(f)),
“and performing the inferencing based on the deployed at least second machine learning model.” (performing inferencing based on a machine learning model is considered mere instructions to apply an exception using generic computer – see MPEP § 2106.05(f))
Since the claim as a whole, looking at the additional elements individually and in combination, does not contain any other additional elements that are indicative of integration into a practical application, the claim is “directed” to an abstract idea.
In step 2B of the 101-analysis set forth in the 2019 PEG, the examiner has determined that the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
As discussed above, additional element vi recites a generic computer component being used as tool to perform functions of the judicial exception, and additional elements vii, viii, ix, and x recite mere instructions to apply the judicial exception using generic computer components, which are not indicative of significantly more.
Considering the additional elements individually and in combination, and the claim as a whole, the additional elements do not provide significantly more than the abstract idea. Therefore, the claim is not patent eligible.
Claim 14:Regarding claim 14, it is dependent upon claim 12, and thereby incorporates the limitations of, and corresponding analysis applied to claim 12. Further claim 14 recites the following abstract idea:
“The system of claim 12, the operations further comprising repeating the comparing operation and creating a new item corresponding to a second intent in response to the similarity score being less than the given threshold and setting a count corresponding to the new item to one” (this is considered a mental process, since a person can mentally evaluate and repeat the comparing limitation from claim 12 to identify a new item based on the chosen mathematical formula to compute a score being less than the threshold of an already established item and incrementing the count for this new item by use of pen and paper, see MPEP § 2106.04(a)(2)(III)),
If claim limitations, under their broadest reasonable interpretation, covers performance of the limitations as a mental process but for the recitation of generic compute components, then it falls within the mental process grouping of abstract ideas. Accordingly, the claim “recites” an abstract idea.
Since the claim does not recite additional elements that either integrate the judicial exception into a practical application, nor provide significantly more than the judicial exception, the claim is not patent eligible.
Claim 15:
Regarding claim 15, it is dependent upon claim 14, and thereby incorporates the limitations of, and corresponding analysis applied to claim 14. Further claim 15 recites the following abstract ideas:
“The system of claim 14, the operations further comprising: clustering client log data into candidate intents and mapping the candidate intents to the one or more items of the matrix” (this is considered a mental process, since a person can mentally evaluate clustering client log data to candidate intents and mapping/matching these intents to items of the matrix established in claim 12, see MPEP § 2106.04(a)(2)(III)),
“creating at least one intent recommendation based on intents corresponding to the one or more items of the training workspace” (this is considered a mental process, since a person can mentally evaluate identifying and creating a recommendation based on intents in correspondence to items from training dataset, see MPEP § 2106.04(a)(2)(III)),
If claim limitations, under their broadest reasonable interpretation, covers performance of the limitations as a mental process but for the recitation of generic compute components, then it falls within the mental process grouping of abstract ideas. Accordingly, the claim “recites” an abstract idea.
Since the claim does not recite additional elements that either integrate the judicial exception into a practical application, nor provide significantly more than the judicial exception, the claim is not patent eligible.
Claim 17:
Regarding claim 17, it is dependent upon claim 14, and thereby incorporates the limitations of, and corresponding analysis applied to claim 14. Further claim 17 recites the following abstract idea:
“The system of claim 14, the operations further comprising repeating the encoding, comparing, mapping, creating the new item, and creating the matrix operations, for each workspace to update the matrix with each pair of training workspace and item” (this is considered a mental process, since a person can mentally evaluate using the encoding, comparing, mapping, creation of a new item, and creation of the matrix limitations from claim 12, to then make updates to the created matrix with each training dataset and item, see MPEP § 2106.04(a)(2)(III)),
If claim limitations, under their broadest reasonable interpretation, covers performance of the limitations as a mental process but for the recitation of generic compute components, then it falls within the mental process grouping of abstract ideas. Accordingly, the claim “recites” an abstract idea.
Since the claim does not recite additional elements that either integrate the judicial exception into a practical application, nor provide significantly more than the judicial exception, the claim is not patent eligible.
Claim 18:
Regarding claim 18, it is dependent upon claim 14, and thereby incorporates the limitations of, and corresponding analysis applied to claim 14. Further claim 18 recites the following abstract ideas:
“The system of claim 14, the operations further comprising: mapping a given client workspace into the matrix” (this is considered a mental process, since a person can mentally evaluate mapping/matching a client dataset into a matrix, see MPEP § 2106.04(a)(2)(III)),
“grouping utterances of client log data into clusters and mapping the clusters into items of the similar training workspace using the corresponding embeddings” (this is considered a mental process, since a person can mentally evaluate grouping utterances of client log data into clusters/groupings and mapping/matching these groupings/clusters into items of the similar training dataset using the corresponding vector representations considered as embeddings, see MPEP § 2106.04(a)(2)(III)),
“recommending the first and second intents corresponding to the mapped items that exist in the similar training workspace and are absent from the given client workspace” (this is considered a mental process, since a person can mentally evaluate and recommend a first and second intent in correspondence to mapped/matched items that exist from the similar training dataset and that are missing from the client dataset, see MPEP § 2106.04(a)(2)(III)),
If claim limitations, under their broadest reasonable interpretation, covers performance of the limitations as a mental process but for the recitation of generic compute components, then it falls within the mental process grouping of abstract ideas. Accordingly, the claim “recites” an abstract idea.
Further, claim 18 recites the following additional element:
“conducting a search of the training workspace that contains a similar set of items compared to the given client workspace” (In step 2A, prong 2, this is considered mere instructions to apply an exception using generic computer with the generate task-specific output operation performed by any generic computer, see MPEP § 2106.05(f)). (In step 2B, this is also considered mere instructions to apply an exception using generic computer - see MPEP § 2106.05(f)).
Since the claim does not recite additional elements that either integrate the judicial exception into a practical application, nor provide significantly more than the judicial exception, the claim is not patent eligible.
Claim 19:
Regarding claim 19, it is dependent upon claim 14, and thereby incorporates the limitations of, and corresponding analysis applied to claim 14. Further claim 19 recites the following abstract ideas:
“The system of claim 14, the operations further comprising: mapping a given client workspace into the matrix” (this is considered a mental process, since a person can mentally evaluate mapping/matching a client dataset into a matrix, see MPEP § 2106.04(a)(2)(III)),
“recommending the first and second intents corresponding to the mapped items that exist in the similar training workspace and are absent from the given client workspace” (this is considered a mental process, since a person can mentally evaluate recommending first and second intents in correspondence to mapped/matched items that exist from the similar training dataset and that are missing from the client dataset, see MPEP § 2106.04(a)(2)(III)),
If claim limitations, under their broadest reasonable interpretation, covers performance of the limitations as a mental process but for the recitation of generic compute components, then it falls within the mental process grouping of abstract ideas. Accordingly, the claim “recites” an abstract idea.
Further, claim 19 recites the following additional element:
“conducting a search of the training workspace(s) that contain a similar set of items in the matrix compared to the given client workspace” (In step 2A, prong 2, this is considered mere instructions to apply an exception using generic computer with the generate task-specific output operation performed by any generic computer, see MPEP § 2106.05(f)). (In step 2B, this is also considered mere instructions to apply an exception using generic computer - see MPEP § 2106.05(f)).
Since the claim does not recite additional elements that either integrate the judicial exception into a practical application, nor provide significantly more than the judicial exception, the claim is not patent eligible.
Claim 20:
Regarding claim 20, it is dependent upon claim 14, and thereby incorporates the limitations of, and corresponding analysis applied to claim 14. Further claim 20 recites the following abstract ideas:
“The system of claim 14, the operations further comprising: grouping utterances of client log data into clusters and mapping the clusters into items of the training workspace using the corresponding intent embeddings” (this is considered a mental process, since a person can mentally evaluate grouping utterances of client log data into clusters/groupings and mapping/matching these groupings/clusters into items of the training datasets using the corresponding vector representations considered as embeddings, see MPEP § 2106.04(a)(2)(III)),
“recommending the first and second intents corresponding to the mapped items that exist in the training workspace” (this is considered a mental process, since a person can mentally evaluate recommending first and second intents in correspondence to mapped/matched items that exist from the training dataset, see MPEP § 2106.04(a)(2)(III)),
If claim limitations, under their broadest reasonable interpretation, covers performance of the limitations as a mental process but for the recitation of generic compute components, then it falls within the mental process grouping of abstract ideas. Accordingly, the claim “recites” an abstract idea.
Since the claim does not recite additional elements that either integrate the judicial exception into a practical application, nor provide significantly more than the judicial exception, the claim is not patent eligible.
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 (i.e., changing from AIA to pre-AIA ) 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.
Claim(s) 1, 3-4, 11-12, and 14-15 are rejected under 35 U.S.C. 103 as being unpatentable over Suwandy T. et al, (US. Patent Application Publication 20210327413 A1) filed on April 4, 2020, (hereafter Suwandy), in view of Cohen W. et al, (US. Patent 8,234,285 B1) filed on July 21, 2009, (hereafter Cohen), further in view of D'Alessandro A. et al, (US. Patent 12,260,387 B2) filed on November 2, 2022, (hereafter D'Alessandro), and further in view of Bravo-Marquez F. et al, " Incremental Word Vectors for Time-Evolving Sentiment Lexicon Induction" available at https://link.springer.com/article/10.1007/s12559-021-09831-y, effectively published on January 12, 2021, (hereafter Bravo-Marquez).
Claim 1:
Regarding claim 1, Suwandy teaches “A method comprising: encoding, using at least one hardware processor, a first intent from customer provided data as an intent embedding;”
See Suwandy in paragraph [0027] where it describes “During operation, the conversational computing service may process incoming natural language user inputs that have been received via conversational entities. When a new natural language input is received, that input may be encoded as an embedding.” Here, Suwandy shows encoding a user input which can be seen as customer provided data as an embedding. Further, see Suwandy in paragraph [0028] describing “Contextual information associated with a natural language input may be taken into account by the conversational computing service in identifying an intent type and/or skill type” Here, Suwandy describes input taken in in the embodiment identifying an intent. Further see Suwandy paragraph [0100] describing, “FIG. 11 is a block diagram illustrating physical components (e.g., hardware) of a computing device 1100 with which aspects of the disclosure may be practiced. The computing device components described below may have computer executable instructions for assisting conversational entity interactions. In a basic configuration, the computing device 1100 may include at least one processing unit 1102 and a system memory 1104.”. Here, Suwandy describes a processor which is implemented in hardware, so it can be seen as a hardware processor capable of performing operations and processes of the embodiment.
Further, Suwandy teaches, “comparing, using the at least one hardware processor, the intent embedding of the first intent and an intent embedding corresponding to one or more items of a training workspace to generate a similarity score,”
See Suwandy in paragraph [0024] where it describes “The conversational computing service may maintain an embedding library that has been curated from one or more data sources. For example, the embedding library may be generated from language received from one or more general dictionaries and/or corpuses, and/or one or more domain-specific resources (e.g., subject-specific dictionaries and corpuses, technical dictionaries and corpuses, individual website domains, individual applications, website domains related to specific search criteria). The language from these sources may be processed with an encoding model to generate embeddings.” Here Suwandy establishes an embedding library which can be viewed herein to contain a training workspace. Further, see Suwandy in paragraph [0026] where it describes “Once the embedding library is generated, a developer associated with a conversational entity (e.g., bot, assistant) may add examples to the library. The examples may include identities of intent types and/or skill types corresponding to intents that a corresponding conversational entity may handle.” Here Suwandy further establishes the embedding library as a training workspace capable of generating examples which can include intents. Further, see Suwandy in paragraph [0027] where it describes “When a new natural language input is received, that input may be encoded as an embedding. In some examples, the embedding may be compressed into a binary form. The embedding may then be added to the embedding library. Once added to the embedding library, a similarity score model may be applied to the new embedding and one or more embeddings from the examples that were provided by the conversational entity developer. In some examples, the similarity score model may be a cosine model. In other examples, the similarity score model may be a Hamming model. A similarity score may be calculated between the new embedding and one or more of the example embeddings.” Here Suwandy shows generating a similarity score based on the new embedding which can be seen as the first intent to other example embeddings apart of the embedding library which can be seen as the training workspace.
However, Suwandy did not explicitly teach “determining whether an existing item of the one or more items has an item embedding corresponding to the similarity score greater than a given threshold; in response to determining that the similarity score is greater than the given threshold, mapping, using the at least one hardware processor, the first intent to the existing item and incrementing a corresponding count of the existing item in the training workspace by one; creating, using the at least one hardware processor, a matrix comprising corresponding counts of the one or more items associated with each of a plurality of training workspaces, wherein the plurality of training workspaces comprises the training workspace; training, using the at least one hardware processor, at least a first machine learning model using one or more of the plurality of training workspaces included in the created matrix; training, using the at least one hardware processor, at least a first machine learning model using one or more of the plurality of training workspaces included in the created matrix; training, using the at least one hardware processor, at least a second machine learning model using the created matrix; deploying, using the at least one hardware processor, the at least second machine learning model; and performing, using the at least one hardware processor, inferencing based on the deployed at least second machine learning model.”
In the same field of art, Cohen teaches, “determining whether an existing item of the one or more items has an item embedding corresponding to the similarity score greater than a given threshold;”
See Cohen in column 1 lines 29-37 describing “A common step in integrating heterogeneous datasets is determining a mapping between objects from one dataset and objects from another dataset. This step is often referred to as record linkage, matching, and/or de-duping. One useful matching strategy is to use a threshold similarity function that generates a similarity score from the feature values and identifies objects as identical if the similarity score exceeds a threshold value.” Here Cohen teaches mapping of one object which can be the existing item to other objects, which can be the one or more items of another dataset if the similarity score exceeds a threshold. In an analogous system we can see the mapping of one object to other objects as mapping an intent and a similar item of one or more items. Further, see Cohen in column 1 lines 61-67 and column 2 lines 1-2 describe “This document describes processes for determining similarity measures for objects in a dataset that include contextual associations of the objects with contexts. A similarity measure for any two objects that include a common feature f is based, in part, on the likelihood that the two object representations in the dataset that both include the feature f will be associated with distinct contexts, and the likelihood that the two objects in the dataset that both include the feature f will be associated with the same context.” Further, see Cohen in column 4 lines 12-18 describing “The contextual similarity engine 112 generates the feature vectors v, and uses the feature vectors to determine the document frequency of the feature values fk with respect to the dataset 120. In particular, with D being the dataset 120 of object instances x1, . . . xn, the document frequency of a feature value f with respect to the dataset 120 ("D") is defined as the number of object instances that include the feature f” Here Cohen shows the object in relation to features and the features having feature vectors, which can be seen as an embedding for object instances, ultimately having two objects or items correspond to each other based on a similarity value or score, established to need an exceeded threshold value passed.
Further, Cohen teaches, “in response to determining that the similarity score is greater than the given threshold, mapping, using the at least one hardware processor, the first intent to the existing item and incrementing a corresponding count of the existing item in the training workspace by one;”
See Cohen in column 1 lines 29-37 describes “A common step in integrating heterogeneous datasets is determining a mapping between objects from one dataset and objects from another dataset. This step is often referred to as record linkage, matching, and/or de-duping. One useful matching strategy is to use a threshold similarity function that generates a similarity score from the feature values and identifies objects as identical if the similarity score exceeds a threshold value.” Here Cohen teaches mapping one object to other objects of another dataset if the similarity score exceeds a threshold. In an analogous system we can see the mapping of one object to other objects as mapping an intent and an existing item. Further, see Cohen in column 12 lines 25-30 describe “For each feature value and context similarity value pair in the context similarity list generated by the process 400, the process 600 increments counter a by CX, increments counter b by CX2, and increments counter c by 1 (604). For example, the contextual similarity engine 112 can increment the counters for each (f, CX) pair.” Here Cohen then teaches incrementing a count based on the mapping corresponding to the similarity score which was earlier established to be determined by exceeding or being greater than a threshold.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the base reference of Suwandy with the teachings of Cohen by using Suwandy’s teachings of converting an intent into and embedding, comparing the embedding, creating a matrix based on a similarity score, and training a model, and incorporate with Cohen’s teachings of mapping items to a similar and incrementing a corresponding count of the similar.
One of ordinary skill in the art would be motivated to do so because by integrating Cohen’s frameworks into the methods of Suwandy, which are both in relation to context analysis and similarity measurement, one of ordinary skill in the art would bring “selecting, by a data processing apparatus, object representations from a dataset storing a plurality of object representations, each object representation being an association of an object identifier that identifies an object instance in the dataset and corresponds to an object, a context value that identifies a context of the object, and a set of feature values that identify features of the object, and wherein each object identifier is unique in the dataset” (Cohen, column 2 lines 5-13).
However, Suwandy in view of Cohen did not explicitly teach “creating, using the at least one hardware processor, a matrix comprising corresponding counts of the one or more items associated with each of a plurality of training workspaces, wherein the plurality of training workspaces comprises the training workspace; training, using the at least one hardware processor, at least a first machine learning model using one or more of the plurality of training workspaces included in the created matrix; training, using the at least one hardware processor, at least a second machine learning model using the created matrix; deploying, using the at least one hardware processor, the at least second machine learning model; and performing, using the at least one hardware processor, inferencing based on the deployed at least second machine learning model.”
Further, D’Alessandro in the same field of art teaches, “training, using the at least one hardware processor, at least a second machine learning model using the created matrix”
See D’Alessandro in Column 5 lines 6-8 where it describes “In some examples, the system includes a machine learning (ML) engine with one or more ML models, which the system may train using training data.” Here, D’Alessandro establishes a ML engine comprising one or more ML models which can be trained using training data which as mentioned before can be seen as training workspaces, the analogous system already teaches a created matrix using training data. Further see D’Alessandro in Column 5 lines 44-47 where it describes “In some examples, the system uses the first trained ML engine to identify the intent for the transaction and uses a second trained ML engine to generate the recommended transaction.” Here D’Alessandro establishes a second trained ML engine and in the analogous system because a ML engine here is trained using training data and it is taught to already create a matrix comprising of training workspaces, we can view this as training a second ML model using a created matrix.
Further, D’Alessandro teaches, “deploying, using the at least one hardware processor, the at least second machine learning model;”
See D’Alessandro in Column 5 lines 44-47 where it describes “In some examples, the system uses the first trained ML engine to identify the intent for the transaction and uses a second trained ML engine to generate the recommended transaction.” Here, D’Alessandro shows a system using or deploying the second machine learning (ML) engine which is using at least another ML model aside from the first ML engine can be used to generate a recommendation.
Further, D’Alessandro teaches “and performing, using the at least one hardware processor, inferencing based on the deployed at least second machine learning model.”
See D’Alessandro in Column 5 lines 46-47 where it describes “In some examples, the system uses the first trained ML engine to identify the intent for the transaction and uses a second trained ML engine to generate the recommended transaction.” Here, D’Alessandro shows using the second machine learning (ML) engine, which is using at least another ML model aside from the first ML engine, to generate a recommendation which can be seen as performing the inferencing.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the base reference of Suwandy with the teachings of Cohen and D’Alessandro by using Suwandy’s teachings of converting an intent into and embedding, comparing the embedding and training a model, and incorporate with Cohen and D’Alessandro’s teachings of mapping items to a similar and incrementing a corresponding count of the similar, creating a matrix based on a similarity score, and using a second trained model to perform inferencing.
One of ordinary skill in the art would be motivated to do so because by integrating D’Alessandro’s frameworks into the methods of Suwandy and Cohen, one with ordinary skill in the art would “provide customized recommendations (e.g., recommended transactions) that are customized and/or tailored specifically to users based on their histories, their account information, intents determined behind their transaction(s), or combinations thereof. This improves over systems that are unable to provide recommendations, or provide standardized recommendations without such customization.” (D’Alessandro, column 6 lines 35-41).
Neither Suwandy, Cohen, or D’Alessandro appear to explicitly teach “creating, using the at least one hardware processor, a matrix comprising corresponding counts of the one or more items associated with each of a plurality of training workspaces, wherein the plurality of training workspaces comprises the training workspace; training, using the at least one hardware processor, at least a first machine learning model using one or more of the plurality of training workspaces included in the created matrix;”
Further, Bravo-Marquez teaches, “creating, using the at least one hardware processor, a matrix comprising corresponding counts of the one or more items associated with each of a plurality of training workspaces, wherein the plurality of training workspaces comprises the training workspace;”
See Bravo-Marquez in Section Proposed Methodology on pages 429-430 describing “The proposed process is illustrated in Fig. 1 and summarized in the following steps: 1. Connect to a stream of continuously arriving text (e.g., Twitter). 2. Every time a new tweet arrives, a sliding window of W words centered on a target word is shifted across the content. 3. The word vector associated with the target word is updated according to its context. This process implies updating word-context counts and computing PPMI based associations. 4. If the target word is new, a new vector associated with this word is created. 5. If the target word is contained in the seed lexicon, the incremental classifier is updated/trained using the target word vector and the lexicon’s sentiment as the gold label. 6. If the target word is not contained in the lexicon, its sentiment is estimated using the classifier and the dynamic lexicon is updated.” Further see Bravo-Marquez Fig 1. On page 430, showing, “
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” Here Bravo-Marquez illustrates correspond counts with items of a matrix with training workspaces with the incremental word-context matrix.
Further, Bravo-Marquez teaches, “training, using the at least one hardware processor, at least a first machine learning model using one or more of the plurality of training workspaces included in the created matrix;”
See Bravo-Marquez in Section Incremental Learning on page 432 describing “An incremental classifier is a supervised model that can learn from new training instances without needing to be retrained on the entire training set from scratch. We use incremental learning to train an adaptive word-level classifier that maps our word vectors into a unidimensional sentiment variable. Our training instances are obtained from a seed lexicon formed by positive and negative words. Every time a word from the seed lexicon is observed, the classifier is updated with a new training instance formed by the current vector of the word and the label provided by the lexicon.” Here Bravo-Marquez shows training of an incremental classifier which is a machine learning model trained using word vectors and training sets or workspaces from the lexicon established to be of the matrix established in previous limitation.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the base references of Suwandy, Cohen and D’Alessandro with the teachings of Bravo-Marquez by using Suwandy’s teachings of converting an intent into and embedding, comparing the embedding and training a model, Cohen and D’Alessandro’s teachings of mapping items to a similar and incrementing a corresponding count of the similar, creating a matrix based on a similarity score, and using a second trained model to perform inferencing, and incorporate with Bravo-Marquez teaching of a created matrix with corresponding counts to items of a training workspace.
One of ordinary skill in the art would be motivated to do so because by integrating Bravo-Marquez’s frameworks into the methods of Suwandy, Cohen, and D’Alessandro, one with ordinary skill in the art would bring, “1. A new methodology for training incremental sentiment lexicons from Twitter streams using incremental word vectors and incremental classifiers. 2. A new technique for evaluating incremental lexicons under scenarios of drastic sentiment drift based on creating synthetic sentences.” (Bravo-Marquez, section Introduction page 427).
Claim 3:
Regarding claim 3, Suwandy in view of Cohen, D’Alessandro and Bravo-Marquez teaches the limitations in claim 1.
Further, Suwandy teaches “method of claim 1, further comprising repeating the comparing operation and creating a new item corresponding to a second intent in response to the similarity score being less than the given threshold…”
See Suwandy in paragraph [0027] where it describes “A similarity score may be calculated between the new embedding and one or more of the example embeddings. A K Nearest Neighbors (KNN) model may then be utilized to identify one or more intent types and/or skill types that correspond to the new embedding. In some examples, a nearest neighbor score value may be calculated for each of the one or more intent types and/or skill types based on application of the KNN model.” Here, Suwandy teaches repeating a comparing operation to attempt to identify one or more intents. Further, see Suwandy in paragraph [0033] where it describes “In some examples, if there is no intent type and/or skill type for which a similarity score exceeds a threshold value, a determination may be made that there is an unknown intent.” Here, Suwandy teaches a new item being created in response to a similarity score not exceeding a threshold making it be less than a threshold, this new item being an unknown intent which is also the second intent.
Further, Cohen teaches “…and setting a count corresponding to the new item to one.”
See Cohen in column 12 lines 25-30 where it describes “For each feature value and context similarity value pair in the context similarity list generated by the process 400, the process 600 increments counter a by CX, increments counter b by CX2, and increments counter c by 1 (604). For example, the contextual similarity engine 112 can increment the counters for each (f, CX) pair. Here, Cohen teaches incrementing a count of a corresponding item to one. In an analogous system one could perform this operation in combination with repeating the comparing operation.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the base reference of Suwandy with the teachings of Cohen by using Suwandy’s teachings of converting an intent into and embedding, comparing the embedding, creating a matrix based on a similarity score, and training a model, and incorporate with Cohen’s teachings of mapping items to a similar and incrementing a corresponding count of the similar.
One of ordinary skill in the art would be motivated to do so because by integrating Cohen’s frameworks into the methods of Suwandy, which are both in relation to context analysis and similarity measurement, one of ordinary skill in the art would bring “selecting, by a data processing apparatus, object representations from a dataset storing a plurality of object representations, each object representation being an association of an object identifier that identifies an object instance in the dataset and corresponds to an object, a context value that identifies a context of the object, and a set of feature values that identify features of the object, and wherein each object identifier is unique in the dataset” (Cohen, column 2 lines 5-13).
Claim 4:
Regarding claim 4, Suwandy in view of Cohen, D’Alessandro and Bravo-Marquez teaches the limitations in claim 3.
Further, Suwandy teaches “The method of claim 3, further comprising: clustering client log data into candidate intents and mapping the candidate intents to the one or more items of the matrix”
See Suwandy paragraph [0026] describing, “Once the embedding library is generated, a developer associated with a conversational entity (e.g., bot, assistant) may add examples to the library. The examples may include identities of intent types and/or skill types corresponding to intents that a corresponding conversational entity may handle. As described herein, an intent comprises a classification of one or more inputs that may be received by a conversational bot, such as into a type of skill or action that may be performed by the conversational bot in response to the input.” Here, Suwandy establishes that the example embeddings can include intents. Further, see Suwandy paragraph [0027] describing, “During operation, the conversational computing service may process incoming natural language user inputs that have been received via conversational entities. When a new natural language input is received, that input may be encoded as an embedding. In some examples, the embedding may be compressed into a binary form. The embedding may then be added to the embedding library. Once added to the embedding library, a similarity score model may be applied to the new embedding and one or more embeddings from the examples that were provided by the conversational entity developer. In some examples, the similarity score model may be a cosine model. In other examples, the similarity score model may be a Hamming model. A similarity score may be calculated between the new embedding and one or more of the example embeddings. A K Nearest Neighbors (KNN) model may then be utilized to identify one or more intent types and/or skill types that correspond to the new embedding.” Here, Suwandy teaches grouping a new embedding which can be seen as an intent from a user input which can be considered client log data into candidate intents by identifying one or more intent types that correspond to the new embedding. The identification of these intents can be seen as mapping the intents to items of the embedding library, which has been established to be considered a matrix, because these intent types come from example embeddings which are items of this matrix.
Further, Suwandy teaches “and creating at least one intent recommendation based on intents corresponding to the one or more items of the training workspace”
See Suwandy paragraph [0027] describing, “In some examples, a nearest neighbor score value may be calculated for each of the one or more intent types and/or skill types based on application of the KNN model. In some examples, if a nearest neighbor score value for an intent type and/or skill type exceeds a threshold value, a response or action corresponding to that intent and/or skill type may be performed by the conversational entity.” Here, Suwandy teaches performing an action using a conversational entity based on an intent being identified. Further See Suwandy paragraph [0027] describing, “In some examples, a nearest neighbor score value may be calculated for each of the one or more intent types and/or skill types based on application of the KNN model. In some examples, if a nearest neighbor score value for an intent type and/or skill type exceeds a threshold value, a response or action corresponding to that intent and/or skill type may be performed by the conversational entity.” Here, Suwandy teaches performing an action or response using a conversational entity based on an intent being identified. Which can be seen as creating an intent recommendation.
Claim 11:
Regarding claim 11, Suwandy teaches “A computer program product, comprising: one or more tangible computer-readable storage media and program instructions stored on at least one of the one or more tangible computer-readable storage media, the program instructions executable by a processor, the program instructions comprising: encoding a first intent from customer provided data as an intent embedding;”
See Suwandy in paragraph [0027] where it describes “During operation, the conversational computing service may process incoming natural language user inputs that have been received via conversational entities. When a new natural language input is received, that input may be encoded as an embedding.” Here, Suwandy shows encoding a user input which can be seen as customer provided data as an embedding. Further, see Suwandy in paragraph [0028] describing “Contextual information associated with a natural language input may be taken into account by the conversational computing service in identifying an intent type and/or skill type” Here, Suwandy describes input taken in in the embodiment identifying an intent. Further see Suwandy paragraph [0100] describing, “FIG. 11 is a block diagram illustrating physical components (e.g., hardware) of a computing device 1100 with which aspects of the disclosure may be practiced. The computing device components described below may have computer executable instructions for assisting conversational entity interactions. In a basic configuration, the computing device 1100 may include at least one processing unit 1102 and a system memory 1104.”. Here, Suwandy describes a processor which is implemented in hardware, so it can be seen as a hardware processor capable of performing operations and processes of the embodiment. Further, see Suwandy in paragraph [0104] describing, “The term computer readable media as used herein may include computer storage media. Computer storage media may include volatile and nonvolatile, removable and non-removable media implemented in any method or technology for storage of information, such as computer readable instructions, data structures, or program modules. The system memory 1104, the removable storage device 1109, and the non-removable storage device 1110 are all computer storage media examples (e.g., memory storage). Computer storage media may include RAM, ROM, electrically erasable read-only memory (EEPROM), flash memory or other memory technology, CD-ROM, digital versatile disks (DVD) or other optical storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other article of manufacture which can be used to store information and which can be accessed by the computing device 1100.” This shows the mentioned computing device 1100 using a tangible computer-readable storage media to further do operations described.
Further, Suwandy teaches, “comparing the intent embedding of the first intent and an intent embedding corresponding to one or more items of a training workspace to generate a similarity score;”
See Suwandy in paragraph [0024] where it describes “The conversational computing service may maintain an embedding library that has been curated from one or more data sources. For example, the embedding library may be generated from language received from one or more general dictionaries and/or corpuses, and/or one or more domain-specific resources (e.g., subject-specific dictionaries and corpuses, technical dictionaries and corpuses, individual website domains, individual applications, website domains related to specific search criteria). The language from these sources may be processed with an encoding model to generate embeddings.” Here Suwandy establishes an embedding library which can be viewed herein to contain a training workspace. Further, see Suwandy in paragraph [0026] where it describes “Once the embedding library is generated, a developer associated with a conversational entity (e.g., bot, assistant) may add examples to the library. The examples may include identities of intent types and/or skill types corresponding to intents that a corresponding conversational entity may handle.” Here Suwandy further establishes the embedding library as a training workspace capable of generating examples which can include intents. Further, see Suwandy in paragraph [0027] where it describes “When a new natural language input is received, that input may be encoded as an embedding. In some examples, the embedding may be compressed into a binary form. The embedding may then be added to the embedding library. Once added to the embedding library, a similarity score model may be applied to the new embedding and one or more embeddings from the examples that were provided by the conversational entity developer. In some examples, the similarity score model may be a cosine model. In other examples, the similarity score model may be a Hamming model. A similarity score may be calculated between the new embedding and one or more of the example embeddings.” Here Suwandy shows generating a similarity score based on the new embedding which can be seen as the first intent to other example embeddings apart of the embedding library which can be seen as the training workspace.
However, Suwandy did not explicitly teach “determining whether an existing item of the one or more items has an item embedding corresponding to the similarity score greater than a given threshold; in response to determining that the similarity score is greater than the given threshold, mapping the first intent to the existing item and incrementing a corresponding count of the existing item in the training workspace by one; creating a matrix, the created matrix comprising corresponding counts of the one or more items associated with each of a plurality of training workspaces, wherein the plurality of training workspaces comprises the training workspace; training at least a first machine learning model using one or more of the plurality of training workspaces included in the created matrix; training at least a second machine learning model using the created matrix; deploying the at least second machine learning model for performing inferencing; and performing the inferencing based on the deployed at least second machine learning model.”
In the same field of art, Cohen teaches, “determining whether an existing item of the one or more items has an item embedding corresponding to the similarity score greater than a given threshold;”
See Cohen in column 1 lines 29-37 describing “A common step in integrating heterogeneous datasets is determining a mapping between objects from one dataset and objects from another dataset. This step is often referred to as record linkage, matching, and/or de-duping. One useful matching strategy is to use a threshold similarity function that generates a similarity score from the feature values and identifies objects as identical if the similarity score exceeds a threshold value.” Here Cohen teaches mapping of one object which can be the existing item to other objects, which can be the one or more items of another dataset if the similarity score exceeds a threshold. In an analogous system we can see the mapping of one object to other objects as mapping an intent and a similar item of one or more items. Further, see Cohen in column 1 lines 61-67 and column 2 lines 1-2 describe “This document describes processes for determining similarity measures for objects in a dataset that include contextual associations of the objects with contexts. A similarity measure for any two objects that include a common feature f is based, in part, on the likelihood that the two object representations in the dataset that both include the feature f will be associated with distinct contexts, and the likelihood that the two objects in the dataset that both include the feature f will be associated with the same context.” Further, see Cohen in column 4 lines 12-18 describing “The contextual similarity engine 112 generates the feature vectors v, and uses the feature vectors to determine the document frequency of the feature values fk with respect to the dataset 120. In particular, with D being the dataset 120 of object instances x1, . . . xn, the document frequency of a feature value f with respect to the dataset 120 ("D") is defined as the number of object instances that include the feature f” Here Cohen shows the object in relation to features and the features having feature vectors, which can be seen as an embedding for object instances, ultimately having two objects or items correspond to each other based on a similarity value or score, established to need an exceeded threshold value passed.
Further, Cohen teaches, “in response to determining that the similarity score is greater than the given threshold, mapping the first intent to the existing item and incrementing a corresponding count of the existing item in the training workspace by one;”
See Cohen in column 1 lines 29-37 describes “A common step in integrating heterogeneous datasets is determining a mapping between objects from one dataset and objects from another dataset. This step is often referred to as record linkage, matching, and/or de-duping. One useful matching strategy is to use a threshold similarity function that generates a similarity score from the feature values and identifies objects as identical if the similarity score exceeds a threshold value.” Here Cohen teaches mapping one object to other objects of another dataset if the similarity score exceeds a threshold. In an analogous system we can see the mapping of one object to other objects as mapping an intent and an existing item. Further, see Cohen in column 12 lines 25-30 describe “For each feature value and context similarity value pair in the context similarity list generated by the process 400, the process 600 increments counter a by CX, increments counter b by CX2, and increments counter c by 1 (604). For example, the contextual similarity engine 112 can increment the counters for each (f, CX) pair.” Here Cohen then teaches incrementing a count based on the mapping corresponding to the similarity score which was earlier established to be determined by exceeding or being greater than a threshold.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the base reference of Suwandy with the teachings of Cohen by using Suwandy’s teachings of converting an intent into and embedding, comparing the embedding, creating a matrix based on a similarity score, and training a model, and incorporate with Cohen’s teachings of mapping items to a similar and incrementing a corresponding count of the similar.
One of ordinary skill in the art would be motivated to do so because by integrating Cohen’s frameworks into the methods of Suwandy, which are both in relation to context analysis and similarity measurement, one of ordinary skill in the art would bring “selecting, by a data processing apparatus, object representations from a dataset storing a plurality of object representations, each object representation being an association of an object identifier that identifies an object instance in the dataset and corresponds to an object, a context value that identifies a context of the object, and a set of feature values that identify features of the object, and wherein each object identifier is unique in the dataset” (Cohen, column 2 lines 5-13).
However, Suwandy in view of Cohen did not explicitly teach “creating a matrix, the created matrix comprising corresponding counts of the one or more items associated with each of a plurality of training workspaces, wherein the plurality of training workspaces comprises the training workspace; training at least a first machine learning model using one or more of the plurality of training workspaces included in the created matrix; training at least a second machine learning model using the created matrix; deploying the at least second machine learning model for performing inferencing; and performing the inferencing based on the deployed at least second machine learning model.”
Further, D’Alessandro in the same field of art teaches, “training at least a second machine learning model using the created matrix”
See D’Alessandro in Column 5 lines 6-8 where it describes “In some examples, the system includes a machine learning (ML) engine with one or more ML models, which the system may train using training data.” Here, D’Alessandro establishes a ML engine comprising one or more ML models which can be trained using training data which as mentioned before can be seen as training workspaces, the analogous system already teaches a created matrix using training data. Further see D’Alessandro in Column 5 lines 44-47 where it describes “In some examples, the system uses the first trained ML engine to identify the intent for the transaction and uses a second trained ML engine to generate the recommended transaction.” Here D’Alessandro establishes a second trained ML engine and in the analogous system because a ML engine here is trained using training data and it is taught to already create a matrix comprising of training workspaces, we can view this as training a second ML model using a created matrix.
Further, D’Alessandro teaches, “deploying the at least second machine learning model for performing inferencing;”
See D’Alessandro in Column 5 lines 44-47 where it describes “In some examples, the system uses the first trained ML engine to identify the intent for the transaction and uses a second trained ML engine to generate the recommended transaction.” Here, D’Alessandro shows a system using or deploying the second machine learning (ML) engine which is using at least another ML model aside from the first ML engine can be used to generate a recommendation and the generated recommendation can be seen as inferencing.
Further, D’Alessandro teaches “and performing the inferencing based on the deployed at least second machine learning model.”
See D’Alessandro in Column 5 lines 46-47 where it describes “In some examples, the system uses the first trained ML engine to identify the intent for the transaction and uses a second trained ML engine to generate the recommended transaction.” Here, D’Alessandro shows using the second machine learning (ML) engine, which is using at least another ML model aside from the first ML engine, to generate a recommendation which can be seen as performing the inferencing.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the base reference of Suwandy with the teachings of Cohen and D’Alessandro by using Suwandy’s teachings of converting an intent into and embedding, comparing the embedding and training a model, and incorporate with Cohen and D’Alessandro’s teachings of mapping items to a similar and incrementing a corresponding count of the similar, and using a second trained model to perform inferencing.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the base reference of Suwandy with the teachings of Cohen and D’Alessandro by using Suwandy’s teachings of converting an intent into and embedding, comparing the embedding and training a model, and incorporate with Cohen and D’Alessandro’s teachings of mapping items to a similar and incrementing a corresponding count of the similar, creating a matrix based on a similarity score, and using a second trained model to perform inferencing.
One of ordinary skill in the art would be motivated to do so because by integrating D’Alessandro’s frameworks into the methods of Suwandy and Cohen, one with ordinary skill in the art would “provide customized recommendations (e.g., recommended transactions) that are customized and/or tailored specifically to users based on their histories, their account information, intents determined behind their transaction(s), or combinations thereof. This improves over systems that are unable to provide recommendations, or provide standardized recommendations without such customization.” (D’Alessandro, column 6 lines 35-41).
Neither Suwandy, Cohen, or D’Alessandro appear to explicitly teach “creating a matrix, the created matrix comprising corresponding counts of the one or more items associated with each of a plurality of training workspaces, wherein the plurality of training workspaces comprises the training workspace; training at least a first machine learning model using one or more of the plurality of training workspaces included in the created matrix;”
However, Bravo-Marquez in the same field of art teaches, “creating a matrix, the created matrix comprising corresponding counts of the one or more items associated with each of a plurality of training workspaces, wherein the plurality of training workspaces comprises the training workspace;”
See Bravo-Marquez in Section Proposed Methodology on pages 429-430 describing “The proposed process is illustrated in Fig. 1 and summarized in the following steps: 1. Connect to a stream of continuously arriving text (e.g., Twitter). 2. Every time a new tweet arrives, a sliding window of W words centered on a target word is shifted across the content. 3. The word vector associated with the target word is updated according to its context. This process implies updating word-context counts and computing PPMI based associations. 4. If the target word is new, a new vector associated with this word is created. 5. If the target word is contained in the seed lexicon, the incremental classifier is updated/trained using the target word vector and the lexicon’s sentiment as the gold label. 6. If the target word is not contained in the lexicon, its sentiment is estimated using the classifier and the dynamic lexicon is updated.” Further see Bravo-Marquez Fig 1. On page 430, showing, “
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” Here Bravo-Marquez illustrates correspond counts with items of a matrix with training workspaces with the incremental word-context matrix.
Further, Bravo-Marquez teaches, “training at least a first machine learning model using one or more of the plurality of training workspaces included in the created matrix;”
See Bravo-Marquez in Section Incremental Learning on page 8 describing “An incremental classifier is a supervised model that can learn from new training instances without needing to be retrained on the entire training set from scratch. We use incremental learning to train an adaptive word-level classifier that maps our word vectors into a unidimensional sentiment variable. Our training instances are obtained from a seed lexicon formed by positive and negative words. Every time a word from the seed lexicon is observed, the classifier is updated with a new training instance formed by the current vector of the word and the label provided by the lexicon.” Here Bravo-Marquez shows training of an incremental classifier which is a machine learning model trained using word vectors and training sets or workspaces from the lexicon established to be of the matrix established in previous limitation.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the base references of Suwandy, Cohen and D’Alessandro with the teachings of Bravo-Marquez by using Suwandy’s teachings of converting an intent into and embedding, comparing the embedding and training a model, Cohen and D’Alessandro’s teachings of mapping items to a similar and incrementing a corresponding count of the similar, creating a matrix based on a similarity score, and using a second trained model to perform inferencing, and incorporate with Bravo-Marquez teaching of a created matrix with corresponding counts to items of a training workspace.
One of ordinary skill in the art would be motivated to do so because by integrating Bravo-Marquez’s frameworks into the methods of Suwandy, Cohen, and D’Alessandro, one with ordinary skill in the art would bring, “1. A new methodology for training incremental sentiment lexicons from Twitter streams using incremental word vectors and incremental classifiers. 2. A new technique for evaluating incremental lexicons under scenarios of drastic sentiment drift based on creating synthetic sentences.” (Bravo-Marquez, section Introduction page 427).
Claim 12:
Regarding claim 11, Suwandy teaches “A system comprising: a memory; and at least one processor, coupled to said memory, and operative to perform operations comprising: encoding a first intent from customer provided data as an intent embedding;”
See Suwandy in paragraph [0027] where it describes “During operation, the conversational computing service may process incoming natural language user inputs that have been received via conversational entities. When a new natural language input is received, that input may be encoded as an embedding.” Here, Suwandy shows encoding a user input which can be seen as customer provided data as an embedding. Further, see Suwandy in paragraph [0028] describing “Contextual information associated with a natural language input may be taken into account by the conversational computing service in identifying an intent type and/or skill type” Here, Suwandy describes input taken in in the embodiment identifying an intent. Further see Suwandy paragraph [0100] describing, “FIG. 11 is a block diagram illustrating physical components (e.g., hardware) of a computing device 1100 with which aspects of the disclosure may be practiced. The computing device components described below may have computer executable instructions for assisting conversational entity interactions. In a basic configuration, the computing device 1100 may include at least one processing unit 1102 and a system memory 1104.”. Here, Suwandy describes a processor which is implemented in hardware, so it can be seen as a hardware processor and a memory together capable of performing operations and processes of the embodiment.
Further, Suwandy teaches, “comparing the intent embedding of the first intent and an intent embedding corresponding to one or more items of a training workspace to generate a similarity score;”
See Suwandy in paragraph [0024] where it describes “The conversational computing service may maintain an embedding library that has been curated from one or more data sources. For example, the embedding library may be generated from language received from one or more general dictionaries and/or corpuses, and/or one or more domain-specific resources (e.g., subject-specific dictionaries and corpuses, technical dictionaries and corpuses, individual website domains, individual applications, website domains related to specific search criteria). The language from these sources may be processed with an encoding model to generate embeddings.” Here Suwandy establishes an embedding library which can be viewed herein to contain a training workspace. Further, see Suwandy in paragraph [0026] where it describes “Once the embedding library is generated, a developer associated with a conversational entity (e.g., bot, assistant) may add examples to the library. The examples may include identities of intent types and/or skill types corresponding to intents that a corresponding conversational entity may handle.” Here Suwandy further establishes the embedding library as a training workspace capable of generating examples which can include intents. Further, see Suwandy in paragraph [0027] where it describes “When a new natural language input is received, that input may be encoded as an embedding. In some examples, the embedding may be compressed into a binary form. The embedding may then be added to the embedding library. Once added to the embedding library, a similarity score model may be applied to the new embedding and one or more embeddings from the examples that were provided by the conversational entity developer. In some examples, the similarity score model may be a cosine model. In other examples, the similarity score model may be a Hamming model. A similarity score may be calculated between the new embedding and one or more of the example embeddings.” Here Suwandy shows generating a similarity score based on the new embedding which can be seen as the first intent to other example embeddings apart of the embedding library which can be seen as the training workspace.
However, Suwandy did not explicitly teach “determining whether an existing item of the one or more items has an item embedding corresponding to the similarity score greater than a given threshold; in response to determining that the similarity score is greater than the given threshold, mapping the first intent to the existing item and incrementing a corresponding count of the existing item in the training workspace by one; creating a matrix, the created matrix comprising corresponding counts of the one or more items associated with each of a plurality of training workspaces, wherein the plurality of training workspaces comprises the training workspace; training at least a first machine learning model using one or more of the plurality of training workspaces included in the created matrix; training at least a second machine learning model using the created matrix; deploying the at least second machine learning model for performing inferencing; and performing the inferencing based on the deployed at least second machine learning model.”
In the same field of art, Cohen teaches, “determining whether an existing item of the one or more items has an item embedding corresponding to the similarity score greater than a given threshold;”
See Cohen in column 1 lines 29-37 describing “A common step in integrating heterogeneous datasets is determining a mapping between objects from one dataset and objects from another dataset. This step is often referred to as record linkage, matching, and/or de-duping. One useful matching strategy is to use a threshold similarity function that generates a similarity score from the feature values and identifies objects as identical if the similarity score exceeds a threshold value.” Here Cohen teaches mapping of one object which can be the existing item to other objects, which can be the one or more items of another dataset if the similarity score exceeds a threshold. In an analogous system we can see the mapping of one object to other objects as mapping an intent and a similar item of one or more items. Further, see Cohen in column 1 lines 61-67 and column 2 lines 1-2 describe “This document describes processes for determining similarity measures for objects in a dataset that include contextual associations of the objects with contexts. A similarity measure for any two objects that include a common feature f is based, in part, on the likelihood that the two object representations in the dataset that both include the feature f will be associated with distinct contexts, and the likelihood that the two objects in the dataset that both include the feature f will be associated with the same context.” Further, see Cohen in column 4 lines 12-18 describing “The contextual similarity engine 112 generates the feature vectors v, and uses the feature vectors to determine the document frequency of the feature values fk with respect to the dataset 120. In particular, with D being the dataset 120 of object instances x1, . . . xn, the document frequency of a feature value f with respect to the dataset 120 ("D") is defined as the number of object instances that include the feature f” Here Cohen shows the object in relation to features and the features having feature vectors, which can be seen as an embedding for object instances, ultimately having two objects or items correspond to each other based on a similarity value or score, established to need an exceeded threshold value passed.
Further, Cohen teaches, “in response to determining that the similarity score is greater than the given threshold, mapping the first intent to the existing item and incrementing a corresponding count of the existing item in the training workspace by one;”
See Cohen in column 1 lines 29-37 describes “A common step in integrating heterogeneous datasets is determining a mapping between objects from one dataset and objects from another dataset. This step is often referred to as record linkage, matching, and/or de-duping. One useful matching strategy is to use a threshold similarity function that generates a similarity score from the feature values and identifies objects as identical if the similarity score exceeds a threshold value.” Here Cohen teaches mapping one object to other objects of another dataset if the similarity score exceeds a threshold. In an analogous system we can see the mapping of one object to other objects as mapping an intent and an existing item. Further, see Cohen in column 12 lines 25-30 describe “For each feature value and context similarity value pair in the context similarity list generated by the process 400, the process 600 increments counter a by CX, increments counter b by CX2, and increments counter c by 1 (604). For example, the contextual similarity engine 112 can increment the counters for each (f, CX) pair.” Here Cohen then teaches incrementing a count based on the mapping corresponding to the similarity score which was earlier established to be determined by exceeding or being greater than a threshold.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the base reference of Suwandy with the teachings of Cohen by using Suwandy’s teachings of converting an intent into and embedding, comparing the embedding, creating a matrix based on a similarity score, and training a model, and incorporate with Cohen’s teachings of mapping items to a similar and incrementing a corresponding count of the similar.
One of ordinary skill in the art would be motivated to do so because by integrating Cohen’s frameworks into the methods of Suwandy, which are both in relation to context analysis and similarity measurement, one of ordinary skill in the art would bring “selecting, by a data processing apparatus, object representations from a dataset storing a plurality of object representations, each object representation being an association of an object identifier that identifies an object instance in the dataset and corresponds to an object, a context value that identifies a context of the object, and a set of feature values that identify features of the object, and wherein each object identifier is unique in the dataset” (Cohen, column 2 lines 5-13).
However, Suwandy in view of Cohen did not explicitly teach “creating a matrix, the created matrix comprising corresponding counts of the one or more items associated with each of a plurality of training workspaces, wherein the plurality of training workspaces comprises the training workspace; training at least a first machine learning model using one or more of the plurality of training workspaces included in the created matrix; training at least a second machine learning model using the created matrix; deploying the at least second machine learning model for performing inferencing; and performing the inferencing based on the deployed at least second machine learning model.”
Further, D’Alessandro in the same field of art teaches, “training at least a second machine learning model using the created matrix”
See D’Alessandro in Column 5 lines 6-8 where it describes “In some examples, the system includes a machine learning (ML) engine with one or more ML models, which the system may train using training data.” Here, D’Alessandro establishes a ML engine comprising one or more ML models which can be trained using training data which as mentioned before can be seen as training workspaces, the analogous system already teaches a created matrix using training data. Further see D’Alessandro in Column 5 lines 44-47 where it describes “In some examples, the system uses the first trained ML engine to identify the intent for the transaction and uses a second trained ML engine to generate the recommended transaction.” Here D’Alessandro establishes a second trained ML engine and in the analogous system because a ML engine here is trained using training data and it is taught to already create a matrix comprising of training workspaces, we can view this as training a second ML model using a created matrix.
Further, D’Alessandro teaches, “deploying the at least second machine learning model for performing inferencing;”
See D’Alessandro in Column 5 lines 44-47 where it describes “In some examples, the system uses the first trained ML engine to identify the intent for the transaction and uses a second trained ML engine to generate the recommended transaction.” Here, D’Alessandro shows a system using or deploying the second machine learning (ML) engine which is using at least another ML model aside from the first ML engine can be used to generate a recommendation and the generated recommendation can be seen as inferencing.
Further, D’Alessandro teaches “and performing the inferencing based on the deployed at least second machine learning model.”
See D’Alessandro in Column 5 lines 46-47 where it describes “In some examples, the system uses the first trained ML engine to identify the intent for the transaction and uses a second trained ML engine to generate the recommended transaction.” Here, D’Alessandro shows using the second machine learning (ML) engine, which is using at least another ML model aside from the first ML engine, to generate a recommendation which can be seen as performing the inferencing.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the base reference of Suwandy with the teachings of Cohen and D’Alessandro by using Suwandy’s teachings of converting an intent into and embedding, comparing the embedding and training a model, and incorporate with Cohen and D’Alessandro’s teachings of mapping items to a similar and incrementing a corresponding count of the similar, and using a second trained model to perform inferencing.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the base reference of Suwandy with the teachings of Cohen and D’Alessandro by using Suwandy’s teachings of converting an intent into and embedding, comparing the embedding and training a model, and incorporate with Cohen and D’Alessandro’s teachings of mapping items to a similar and incrementing a corresponding count of the similar, creating a matrix based on a similarity score, and using a second trained model to perform inferencing.
One of ordinary skill in the art would be motivated to do so because by integrating D’Alessandro’s frameworks into the methods of Suwandy and Cohen, one with ordinary skill in the art would “provide customized recommendations (e.g., recommended transactions) that are customized and/or tailored specifically to users based on their histories, their account information, intents determined behind their transaction(s), or combinations thereof. This improves over systems that are unable to provide recommendations, or provide standardized recommendations without such customization.” (D’Alessandro, column 6 lines 35-41).
Neither Suwandy, Cohen, or D’Alessandro appear to explicitly teach “creating a matrix, the created matrix comprising corresponding counts of the one or more items associated with each of a plurality of training workspaces, wherein the plurality of training workspaces comprises the training workspace; training at least a first machine learning model using one or more of the plurality of training workspaces included in the created matrix;”
However, Bravo-Marquez in the same field of art teaches, “creating a matrix, the created matrix comprising corresponding counts of the one or more items associated with each of a plurality of training workspaces, wherein the plurality of training workspaces comprises the training workspace;”
See Bravo-Marquez in Section Proposed Methodology on pages 429-430 describing “The proposed process is illustrated in Fig. 1 and summarized in the following steps: 1. Connect to a stream of continuously arriving text (e.g., Twitter). 2. Every time a new tweet arrives, a sliding window of W words centered on a target word is shifted across the content. 3. The word vector associated with the target word is updated according to its context. This process implies updating word-context counts and computing PPMI based associations. 4. If the target word is new, a new vector associated with this word is created. 5. If the target word is contained in the seed lexicon, the incremental classifier is updated/trained using the target word vector and the lexicon’s sentiment as the gold label. 6. If the target word is not contained in the lexicon, its sentiment is estimated using the classifier and the dynamic lexicon is updated.” Further see Bravo-Marquez Fig 1. On page 430, showing, “
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” Here Bravo-Marquez illustrates correspond counts with items of a matrix with training workspaces with the incremental word-context matrix.
Further, Bravo-Marquez teaches, “training at least a first machine learning model using one or more of the plurality of training workspaces included in the created matrix;”
See Bravo-Marquez in Section Incremental Learning on page 8 describing “An incremental classifier is a supervised model that can learn from new training instances without needing to be retrained on the entire training set from scratch. We use incremental learning to train an adaptive word-level classifier that maps our word vectors into a unidimensional sentiment variable. Our training instances are obtained from a seed lexicon formed by positive and negative words. Every time a word from the seed lexicon is observed, the classifier is updated with a new training instance formed by the current vector of the word and the label provided by the lexicon.” Here Bravo-Marquez shows training of an incremental classifier which is a machine learning model trained using word vectors and training sets or workspaces from the lexicon established to be of the matrix established in previous limitation.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the base references of Suwandy, Cohen and D’Alessandro with the teachings of Bravo-Marquez by using Suwandy’s teachings of converting an intent into and embedding, comparing the embedding and training a model, Cohen and D’Alessandro’s teachings of mapping items to a similar and incrementing a corresponding count of the similar, creating a matrix based on a similarity score, and using a second trained model to perform inferencing, and incorporate with Bravo-Marquez teaching of a created matrix with corresponding counts to items of a training workspace.
One of ordinary skill in the art would be motivated to do so because by integrating Bravo-Marquez’s frameworks into the methods of Suwandy, Cohen, and D’Alessandro, one with ordinary skill in the art would bring, “1. A new methodology for training incremental sentiment lexicons from Twitter streams using incremental word vectors and incremental classifiers. 2. A new technique for evaluating incremental lexicons under scenarios of drastic sentiment drift based on creating synthetic sentences.” (Bravo-Marquez, section Introduction page 427).
Claim 14:
Regarding claim 14, Suwandy in view of Cohen, D’Alessandro and Bravo-Marquez teaches the limitations in claim 12.
Further, Suwandy teaches “The system of claim 12, the operations further comprising repeating the comparing operation and creating a new item corresponding to a second intent in response to the similarity score being less than the given threshold”
See Suwandy in paragraph [0027] where it describes “A similarity score may be calculated between the new embedding and one or more of the example embeddings. A K Nearest Neighbors (KNN) model may then be utilized to identify one or more intent types and/or skill types that correspond to the new embedding. In some examples, a nearest neighbor score value may be calculated for each of the one or more intent types and/or skill types based on application of the KNN model.” Here, Suwandy teaches repeating a comparing operation to attempt to identify one or more intents. Further, see Suwandy in paragraph [0033] where it describes “In some examples, if there is no intent type and/or skill type for which a similarity score exceeds a threshold value, a determination may be made that there is an unknown intent.” Here, Suwandy teaches a new item being created in response to a similarity score not exceeding a threshold making it be less than a threshold, this new item being an unknown intent which is also the second intent.
Further, Cohen teaches “and setting a count corresponding to the new item to one.”
See Cohen in column 12 lines 25-30 where it describes “For each feature value and context similarity value pair in the context similarity list generated by the process 400, the process 600 increments counter a by CX, increments counter b by CX2 and increments counter c by 1 (604). For example, the contextual similarity engine 112 can increment the counters for each (f, CX) pair. Here, Cohen teaches incrementing a count of a corresponding item to one. In an analogous system one could perform this operation in combination with repeating the comparing operation.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the base reference of Suwandy with the teachings of Cohen by using Suwandy’s teachings of converting an intent into and embedding, comparing the embedding, creating a matrix based on a similarity score, and training a model, and incorporate with Cohen’s teachings of mapping items to a similar and incrementing a corresponding count of the similar.
One of ordinary skill in the art would be motivated to do so because by integrating Cohen’s frameworks into the methods of Suwandy, which are both in relation to context analysis and similarity measurement, one of ordinary skill in the art would bring “selecting, by a data processing apparatus, object representations from a dataset storing a plurality of object representations, each object representation being an association of an object identifier that identifies an object instance in the dataset and corresponds to an object, a context value that identifies a context of the object, and a set of feature values that identify features of the object, and wherein each object identifier is unique in the dataset” (Cohen, column 2 lines 5-13).
Claim 15:
Regarding claim 15, Suwandy in view of Cohen, D’Alessandro and Bravo-Marquez teaches the limitations in claim 14.
Further, Suwandy teaches “The system of claim 14, the operations further comprising: clustering client log data into candidate intents and mapping the candidate intents to the one or more items of the matrix”
See Suwandy paragraph [0026] describing, “Once the embedding library is generated, a developer associated with a conversational entity (e.g., bot, assistant) may add examples to the library. The examples may include identities of intent types and/or skill types corresponding to intents that a corresponding conversational entity may handle. As described herein, an intent comprises a classification of one or more inputs that may be received by a conversational bot, such as into a type of skill or action that may be performed by the conversational bot in response to the input.” Here, Suwandy establishes that the example embeddings can include intents. Further, see Suwandy paragraph [0027] describing, “During operation, the conversational computing service may process incoming natural language user inputs that have been received via conversational entities. When a new natural language input is received, that input may be encoded as an embedding. In some examples, the embedding may be compressed into a binary form. The embedding may then be added to the embedding library. Once added to the embedding library, a similarity score model may be applied to the new embedding and one or more embeddings from the examples that were provided by the conversational entity developer. In some examples, the similarity score model may be a cosine model. In other examples, the similarity score model may be a Hamming model. A similarity score may be calculated between the new embedding and one or more of the example embeddings. A K Nearest Neighbors (KNN) model may then be utilized to identify one or more intent types and/or skill types that correspond to the new embedding.” Here, Suwandy teaches grouping a new embedding which can be seen as an intent from a user input which can be considered client log data into candidate intents by identifying one or more intent types that correspond to the new embedding. The identification of these intents can be seen as mapping the intents to items of the embedding library, which has been established to be considered a matrix, because these intent types come from example embeddings which are items of this matrix.
Further, Suwandy teaches “creating at least one intent recommendation based on intents corresponding to the one or more items of the training workspace”
See Suwandy paragraph [0027] describing, “In some examples, a nearest neighbor score value may be calculated for each of the one or more intent types and/or skill types based on application of the KNN model. In some examples, if a nearest neighbor score value for an intent type and/or skill type exceeds a threshold value, a response or action corresponding to that intent and/or skill type may be performed by the conversational entity.” Here, Suwandy teaches performing an action using a conversational entity based on an intent being identified. Further See Suwandy paragraph [0027] describing, “In some examples, a nearest neighbor score value may be calculated for each of the one or more intent types and/or skill types based on application of the KNN model. In some examples, if a nearest neighbor score value for an intent type and/or skill type exceeds a threshold value, a response or action corresponding to that intent and/or skill type may be performed by the conversational entity.” Here, Suwandy teaches performing an action or response using a conversational entity based on an intent being identified. Which can be seen as creating an intent recommendation.
Claim(s) 6 and 17 are rejected under 35 U.S.C. 103 as being unpatentable over Suwandy T. et al, in view of Cohen W. et al, further in view of D'Alessandro A. et al, further in view of Bravo-Marquez F. et al, and further in view of Freed R. et al, (US. Patent 11,756,553 B2) filed on September 7, 2020.
Claim 6:
Regarding claim 6, Suwandy in view of Cohen, D’Alessandro and Bravo-Marquez teaches the limitations in claim 3.
Neither Suwandy, Cohen, D’Alessandro, or Bravo-Marquez appears to teach the further limitations of this claim. However, Freed in the same field of art teaches “The method of claim 3, further comprising repeating the encoding, comparing, mapping, creating the new item, and creating [[the ]]matrix operations, for each workspace to update the matrix with each pair of training workspace and item”
See Freed column 5 lines 47-67 and column 6 lines 1-9 where it describes “In one or more embodiments, data enhancement module 110 is configured to generate a set of new training data based on the set of new intents and entities for chatbot server 104. Data enhancement module 110 may perform a test (e.g., a k-fold test or k-fold cross-validation) of the set of training data and the set of new training data. For example, k-fold cross-validation may be where a given data set is split into a K number of sections/folds where each fold is used as a testing set at some point. In the first iteration, the first fold may be used to test the model and the rest may be used to train the model. In the second iteration, 2nd fold is used as the testing set while the rest serve as the training set. This process is repeated until each fold has been used as the testing set. Data enhancement module 110 may analyze the tested results. Data enhancement module 110 may compare the testing results. Data enhancement module 110 may test the new training data against a k-fold testing algorithm to determine a validity score (needed to map to a new intent) of the new training data. Data enhancement module 110 may perform an action based on the validity score. Data enhancement module 110 may compare the test results by noting that an utterance previously resolved to a previous intent now needs to be resolved to a new intent. Data enhancement module 110 may provide a manual review indication for an utterance that does not include any entity listed. Data enhancement module 110 may automatically update the training for chatbot server 104 with the new training data when data enhancement module 110 determines the accuracy based on the testing of the new training data is maintained or improved.” Here, Freed teaches a process of repeating taking in new inputs, which in an analogous system could be encoding a user intent, comparing, testing new training data to map to an intent, resolving to a new intent which can be seen as creating a new item, and does the creating matrix operations described before. The repeat of these operations update the training data(s) of the data enhancement module to then update the chatbot server, which can be a matrix as known in the art.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the base reference of Suwandy, Cohen, D’Alessandro, and Bravo-Marquez into the methods of Freed by using Suwandy’s teachings of converting an intent into and embedding, comparing the embedding and training a model, with Cohen, D’Alessandro and Bravo-Marquez’s teachings of mapping items to a similar and incrementing a corresponding count of the similar, and using a second trained model to perform inferencing, and a created matrix with corresponding counts to items of a training workspace and further incorporate further Freed’s teachings of repeating operations to update workspaces of a matrix.
One of ordinary skill in the art would be motivated to do so because by integrating Freed’s frameworks into the methods of Suwandy, Cohen, D’Alessandro, and Bravo-Marquez, one with ordinary skill in the art would bring “performing an evidence-based analysis of a chatbot's existing training data and identifying opportunities for improvement by identifying intents that can be refactored into intents with entities.” (Freed, column 1 lines 66-67 and column 2 lines 1-3), “taking utterances that a user intends to route differently in the chatbot and optimizing the use of intents and entities to do that routing.” (Freed, column 2 lines 4-6), “maintaining and improving a testing score (e.g., a k-fold score) of a training data set with the intention of increasing a blind score by being more generalizable and less confusable” (Freed, column 2 lines 7-10), and “improving training data by moving from more intents/few entities to less intents/more entities.” (Freed, column 2 lines 11-12).
Claim 17:
Regarding claim 17, Suwandy in view of Cohen, D’Alessandro and Bravo-Marquez teaches the limitations in claim 14.
Neither Suwandy, Cohen, D’Alessandro, or Bravo-Marquez appears to teach the further limitations of this claim. However, Freed in the same field of art teaches “The system of claim 14, the operations further comprising repeating the encoding, comparing, mapping, creating the new item, and creating [[the ]]matrix operations, for each workspace to update the matrix with each pair of training workspace and item”
See Freed column 5 lines 47-67 and column 6 lines 1-9 where it describes “In one or more embodiments, data enhancement module 110 is configured to generate a set of new training data based on the set of new intents and entities for chatbot server 104. Data enhancement module 110 may perform a test (e.g., a k-fold test or k-fold cross-validation) of the set of training data and the set of new training data. For example, k-fold cross-validation may be where a given data set is split into a K number of sections/folds where each fold is used as a testing set at some point. In the first iteration, the first fold may be used to test the model and the rest may be used to train the model. In the second iteration, 2nd fold is used as the testing set while the rest serve as the training set. This process is repeated until each fold has been used as the testing set. Data enhancement module 110 may analyze the tested results. Data enhancement module 110 may compare the testing results. Data enhancement module 110 may test the new training data against a k-fold testing algorithm to determine a validity score (needed to map to a new intent) of the new training data. Data enhancement module 110 may perform an action based on the validity score. Data enhancement module 110 may compare the test results by noting that an utterance previously resolved to a previous intent now needs to be resolved to a new intent. Data enhancement module 110 may provide a manual review indication for an utterance that does not include any entity listed. Data enhancement module 110 may automatically update the training for chatbot server 104 with the new training data when data enhancement module 110 determines the accuracy based on the testing of the new training data is maintained or improved.” Here, Freed teaches a process of repeating taking in new inputs, which in an analogous system could be encoding a user intent, comparing, testing new training data to map to an intent, resolving to a new intent which can be seen as creating a new item, and does the creating matrix operations described before. The repeat of these operations update the training data(s) of the data enhancement module to then update the chatbot server, which can be a matrix as known in the art.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the base reference of Suwandy, Cohen, D’Alessandro, and Bravo-Marquez into the methods of Freed by using Suwandy’s teachings of converting an intent into and embedding, comparing the embedding and training a model, with Cohen, D’Alessandro and Bravo-Marquez’s teachings of mapping items to a similar and incrementing a corresponding count of the similar, and using a second trained model to perform inferencing, and a created matrix with corresponding counts to items of a training workspace and further incorporate further Freed’s teachings of repeating operations to update workspaces of a matrix.
One of ordinary skill in the art would be motivated to do so because by integrating Freed’s frameworks into the methods of Suwandy, Cohen, D’Alessandro, and Bravo-Marquez, one with ordinary skill in the art would bring “performing an evidence-based analysis of a chatbot's existing training data and identifying opportunities for improvement by identifying intents that can be refactored into intents with entities.” (Freed, column 1 lines 66-67 and column 2 lines 1-3), “taking utterances that a user intends to route differently in the chatbot and optimizing the use of intents and entities to do that routing.” (Freed, column 2 lines 4-6), “maintaining and improving a testing score (e.g., a k-fold score) of a training data set with the intention of increasing a blind score by being more generalizable and less confusable” (Freed, column 2 lines 7-10), and “improving training data by moving from more intents/few entities to less intents/more entities.” (Freed, column 2 lines 11-12).
Claim(s) 7-9 and 18-20 are rejected under 35 U.S.C. 103 as being unpatentable over Suwandy T. et al, in view of Cohen W. et al l, further in view of D'Alessandro A. et al, further in view of Bravo-Marquez F. et al, and further in view of Kumar D. et al, (US. Patent Application Publication 20240419901 A1) filed on June 15, 2023.
Claim 7:
Regarding claim 7, Suwandy in view of Cohen, D’Alessandro and Bravo-Marquez teaches the limitations in claim 3.
Further, Suwandy teaches “The method of claim 3, further comprising: mapping a given client workspace into the matrix”
See Suwandy in paragraph [0027] describing “During operation, the conversational computing service may process incoming natural language user inputs that have been received via conversational entities. When a new natural language input is received, that input may be encoded as an embedding. In some examples, the embedding may be compressed into a binary form. The embedding may then be added to the embedding library.” Here, Suwandy maps a client workspace into a matrix by taking in user inputs which can be seen as a client workspace, converting it into an embedding and then adding it to an embedding library, which previously is stated to already be perceived as a matrix, which can be seen as mapping into a matrix.
Further, Suwandy teaches, “conducting a search of a training workspace that contains a similar set of items compared to the given client workspace”
See Suwandy in paragraph [0027] where it describes “During operation, the conversational computing service may process incoming natural language user inputs that have been received via conversational entities. When a new natural language input is received, that input may be encoded as an embedding. In some examples, the embedding may be compressed into a binary form. The embedding may then be added to the embedding library. Once added to the embedding library, a similarity score model may be applied to the new embedding and one or more embeddings from the examples that were provided by the conversational entity developer. In some examples, the similarity score model may be a cosine model. In other examples, the similarity score model may be a Hamming model. A similarity score may be calculated between the new embedding and one or more of the example embeddings. A K Nearest Neighbors (KNN) model may then be utilized to identify one or more intent types and/or skill types that correspond to the new embedding. In some examples, a nearest neighbor score value may be calculated for each of the one or more intent types and/or skill types based on application of the KNN model.” Here, Suwandy shows using a K Nearest Neighbors model, which is used to perform searches as understood in the art, to look for intents that correspond to the new embedding which in this instance is the client workspace as mentioned. The search for similar items is done using similarity scores and nearest neighbor score.
Neither Suwandy, Cohen, D’Alessandro, or Bravo-Marquez appears to teach the “grouping utterances of client log data into clusters and mapping the clusters into items of the similar training workspace using the corresponding intent embeddings; recommending the first and second intents corresponding to the mapped items that exist in the similar training workspace and are absent from the given client workspace.”
However, Kumar in the same field of art teaches, “grouping utterances of client log data into clusters and mapping the clusters into items of the similar training workspace using the corresponding intent embeddings;”
See Kumar in paragraph [0017] describing, “In some implementations, when preprocessing the text data with the one or more preprocessing techniques to generate the key intents, the transformation system may identify utterances in the text data, and may generate parts of speech tags for the text data (e.g., by assigning parts of speech tags that correspond to particular parts of speech, such as nouns, verbs, adjectives, adverbs, and/or the like).” Here, Kumar establishes utterances being a part of the text data which can be used to generate intents, in these, these utterances are grouped. Further, see Kumar in paragraph [0018] describing, “the transformation system 110 may convert the preprocessed data and the key intents into embeddings.” The intents mentioned can be turned into embeddings. Further, see Kumar in Figure 2,
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. In this figure given by Kumar, we can see clusters of sets of observations which as mentioned before can be seen as training workspaces. The new observation, which also as mentioned can be seen as a client workspace consists of intents which come from utterances being mapped to other observations.
Further, Kumar teaches, “recommending the first and second intents corresponding to the mapped items that exist in the similar training workspace and are absent from the given client workspace.”
See Kumar in paragraph [0041], where it describes “In some implementations, the trained machine learning model 225 may classify (e.g., cluster) the new observation in a cluster, as shown by reference number 240. The observations within a cluster may have a threshold degree of similarity. As an example, if the machine learning system classifies the new observation in a first cluster (e.g., a preprocessed data cluster), then the machine learning system may provide a first recommendation.” Further, see Kumar in paragraph [0042] describing, “As another example, if the machine learning system were to classify the new observation in a second cluster (e.g., a key intents cluster), then the machine learning system may provide a second (e.g., different) recommendation” Here, for the new observation, as mentioned before being seen as a client workspace, if the recommendation is made using clusters instead of the new observation in itself it can be seen that the recommendation exist in a similar training workspace and is absent from the client workspace. Kumar here also teaches a first and second recommendation being made off intents.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the base reference of Suwandy, Cohen, D’Alessandro, and Bravo-Marquez into the methods of Freed by using Suwandy’s teachings of converting an intent into and embedding, comparing the embedding and training a model, with Cohen, D’Alessandro and Bravo-Marquez’s teachings of mapping items to a similar and incrementing a corresponding count of the similar, and using a second trained model to perform inferencing, and a created matrix with corresponding counts to items of a training workspace and further incorporate further Freed’s teachings of repeating operations to update workspaces of a matrix.
One of ordinary skill in the art would be motivated to do so because by integrating Freed’s frameworks into the methods of Suwandy, Cohen, D’Alessandro, and Bravo-Marquez, one with ordinary skill in the art would bring a “transformation system [that] generates a conversation summary from text data using a language transformation model” (Kumar, paragraph [0011]), “transformation system [that] may process the preprocessed data and the key intents, with a language model, to generate a conversation summary for the transcript. The transformation system may utilize the conversation summary to understand a customer journey, a customer issue, and/or a customer need, to provide an improved search experience, to identify novel categories, and/or the like” (Kumar, paragraph [0011]), and a “transformation system [that] may conserve computing resources, networking resources, and/or other resources that would have otherwise been consumed by failing to identify meaningful information and/or phrases in text data, failing to identify summaries of conversations provided in text data, being unable to utilize the meaningful information and/or phrases and the summaries of conversations, and/or the like.” (Kumar, paragraph [0011]).
Claim 8:
Regarding claim 8, Suwandy in view of Cohen, D’Alessandro and Bravo-Marquez teaches the limitations in claim 3.
Further, Suwandy teaches “The method of claim 3, further comprising: mapping a given client workspace into the matrix”
See Suwandy in paragraph [0027] describing “During operation, the conversational computing service may process incoming natural language user inputs that have been received via conversational entities. When a new natural language input is received, that input may be encoded as an embedding. In some examples, the embedding may be compressed into a binary form. The embedding may then be added to the embedding library.” Here, Suwandy maps a client workspace into a matrix by taking in user inputs which can be seen as a client workspace, converting it into an embedding and then adding it to an embedding library, which previously is stated to already be perceived as a matrix, which can be seen as mapping into a matrix.
Further, Suwandy teaches, “conducting a search of the training workspace(s) that contain a similar set of items in the matrix compared to the given client workspace”
See Suwandy in paragraph [0027] where it describes “During operation, the conversational computing service may process incoming natural language user inputs that have been received via conversational entities. When a new natural language input is received, that input may be encoded as an embedding. In some examples, the embedding may be compressed into a binary form. The embedding may then be added to the embedding library. Once added to the embedding library, a similarity score model may be applied to the new embedding and one or more embeddings from the examples that were provided by the conversational entity developer. In some examples, the similarity score model may be a cosine model. In other examples, the similarity score model may be a Hamming model. A similarity score may be calculated between the new embedding and one or more of the example embeddings. A K Nearest Neighbors (KNN) model may then be utilized to identify one or more intent types and/or skill types that correspond to the new embedding. In some examples, a nearest neighbor score value may be calculated for each of the one or more intent types and/or skill types based on application of the KNN model.” Here, Suwandy shows using a K Nearest Neighbors model, which is used to perform searches as understood in the art, to look for intents that correspond to the new embedding which in this instance is the client workspace as mentioned. The search for similar items is done using similarity scores and nearest neighbor score.
Neither Suwandy, Cohen, D’Alessandro, or Bravo-Marquez appears to teach the “recommending the first and second intents corresponding to the mapped items that exist in the similar training workspace and are absent from the given client workspace”.
However, Kumar in the same field of art teaches, “recommending the first and second intents corresponding to the mapped items that exist in the similar training workspace and are absent from the given client workspace”
See Kumar in paragraph [0017] describing, “In some implementations, when preprocessing the text data with the one or more preprocessing techniques to generate the key intents, the transformation system may identify utterances in the text data, and may generate parts of speech tags for the text data (e.g., by assigning parts of speech tags that correspond to particular parts of speech, such as nouns, verbs, adjectives, adverbs, and/or the like).” Here, Kumar establishes utterances being a part of the text data which can be used to generate intents, in these, these utterances are grouped. Further, see Kumar in paragraph [0018] describing, “the transformation system 110 may convert the preprocessed data and the key intents into embeddings.” The intents mentioned can be turned into embeddings. Further, see Kumar in Figure 2,
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397
491
media_image2.png
Greyscale
. In this figure given by Kumar, we can see clusters of sets of observations which as mentioned before can be seen as training workspaces. The new observation, which also as mentioned can be seen as a client workspace consists of intents which come from utterances being mapped to other observations.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the base reference of Suwandy, Cohen, D’Alessandro, and Bravo-Marquez into the methods of Freed by using Suwandy’s teachings of converting an intent into and embedding, comparing the embedding and training a model, with Cohen, D’Alessandro and Bravo-Marquez’s teachings of mapping items to a similar and incrementing a corresponding count of the similar, and using a second trained model to perform inferencing, and a created matrix with corresponding counts to items of a training workspace and further incorporate further Freed’s teachings of repeating operations to update workspaces of a matrix.
One of ordinary skill in the art would be motivated to do so because by integrating Freed’s frameworks into the methods of Suwandy, Cohen, D’Alessandro, and Bravo-Marquez, one with ordinary skill in the art would bring a “transformation system [that] generates a conversation summary from text data using a language transformation model” (Kumar, paragraph [0011]), “transformation system [that] may process the preprocessed data and the key intents, with a language model, to generate a conversation summary for the transcript. The transformation system may utilize the conversation summary to understand a customer journey, a customer issue, and/or a customer need, to provide an improved search experience, to identify novel categories, and/or the like” (Kumar, paragraph [0011]), and a “transformation system [that] may conserve computing resources, networking resources, and/or other resources that would have otherwise been consumed by failing to identify meaningful information and/or phrases in text data, failing to identify summaries of conversations provided in text data, being unable to utilize the meaningful information and/or phrases and the summaries of conversations, and/or the like.” (Kumar, paragraph [0011]).
Claim 9:
Regarding claim 9, Suwandy in view of Cohen, D’Alessandro and Bravo-Marquez teaches the limitations in claim 3.
Neither Suwandy, Cohen, D’Alessandro, or Bravo-Marquez appears to teach the further limitations of this claim. However, Kumar in the same field of art teaches, “The method of claim 3, further comprising: grouping utterances of client log data into clusters and mapping the clusters into items of the training workspace using the corresponding intent embeddings”
See Kumar in paragraph [0017] describing, “In some implementations, when preprocessing the text data with the one or more preprocessing techniques to generate the key intents, the transformation system may identify utterances in the text data, and may generate parts of speech tags for the text data (e.g., by assigning parts of speech tags that correspond to particular parts of speech, such as nouns, verbs, adjectives, adverbs, and/or the like).” Here, Kumar establishes utterances being a part of the text data which can be used to generate intents, in these, these utterances are grouped. Further, see Suwandy in paragraph [0018] describing, “the transformation system 110 may convert the preprocessed data and the key intents into embeddings.” The intents mentioned can be turned into embeddings. Further, see Kumar in Figure 2,
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397
491
media_image2.png
Greyscale
. In this figure given by Kumar, we can see clusters of sets of observations which as mentioned before can be seen as training workspaces. The new observation, which also as mentioned can be seen as a client workspace consists of intents which come from utterances being mapped to other observations.
Further, Kumar teaches, “and recommending the first and second intents corresponding to the mapped items that exist in the training workspace.”
See Kumar in paragraph [0041], where it describes “In some implementations, the trained machine learning model 225 may classify (e.g., cluster) the new observation in a cluster, as shown by reference number 240. The observations within a cluster may have a threshold degree of similarity. As an example, if the machine learning system classifies the new observation in a first cluster (e.g., a preprocessed data cluster), then the machine learning system may provide a first recommendation.” Further, see Kumar in paragraph [0042] describing, “As another example, if the machine learning system were to classify the new observation in a second cluster (e.g., a key intents cluster), then the machine learning system may provide a second (e.g., different) recommendation” Here, for the new observation, as mentioned before being seen as a client workspace, if the recommendation is made using clusters instead of the new observation in itself it can be seen that the recommendation exist in a similar training workspace. Suwandy here also teaches a first and second recommendation being made off intents.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the base reference of Suwandy, Cohen, D’Alessandro, and Bravo-Marquez into the methods of Freed by using Suwandy’s teachings of converting an intent into and embedding, comparing the embedding and training a model, with Cohen, D’Alessandro and Bravo-Marquez’s teachings of mapping items to a similar and incrementing a corresponding count of the similar, and using a second trained model to perform inferencing, and a created matrix with corresponding counts to items of a training workspace and further incorporate further Freed’s teachings of repeating operations to update workspaces of a matrix.
One of ordinary skill in the art would be motivated to do so because by integrating Freed’s frameworks into the methods of Suwandy, Cohen, D’Alessandro, and Bravo-Marquez, one with ordinary skill in the art would bring a “transformation system [that] generates a conversation summary from text data using a language transformation model” (Kumar, paragraph [0011]), “transformation system [that] may process the preprocessed data and the key intents, with a language model, to generate a conversation summary for the transcript. The transformation system may utilize the conversation summary to understand a customer journey, a customer issue, and/or a customer need, to provide an improved search experience, to identify novel categories, and/or the like” (Kumar, paragraph [0011]), and a “transformation system [that] may conserve computing resources, networking resources, and/or other resources that would have otherwise been consumed by failing to identify meaningful information and/or phrases in text data, failing to identify summaries of conversations provided in text data, being unable to utilize the meaningful information and/or phrases and the summaries of conversations, and/or the like.” (Kumar, paragraph [0011]).
Claim 18:
Regarding claim 18, Suwandy in view of Cohen, D’Alessandro and Bravo-Marquez teaches the limitations in claim 14.
Further, Suwandy teaches “The system of claim 14, the operations further comprising: mapping a given client workspace into the matrix”
See Suwandy in paragraph [0027] describing “During operation, the conversational computing service may process incoming natural language user inputs that have been received via conversational entities. When a new natural language input is received, that input may be encoded as an embedding. In some examples, the embedding may be compressed into a binary form. The embedding may then be added to the embedding library.” Here, Suwandy maps a client workspace into a matrix by taking in user inputs which can be seen as a client workspace, converting it into an embedding and then adding it to an embedding library, which previously is stated to already be perceived as a matrix, which can be seen as mapping into a matrix.
Further, Suwandy teaches, “conducting a search of the training workspace that contains a similar set of items compared to the given client workspace”
See Suwandy in paragraph [0027] where it describes “During operation, the conversational computing service may process incoming natural language user inputs that have been received via conversational entities. When a new natural language input is received, that input may be encoded as an embedding. In some examples, the embedding may be compressed into a binary form. The embedding may then be added to the embedding library. Once added to the embedding library, a similarity score model may be applied to the new embedding and one or more embeddings from the examples that were provided by the conversational entity developer. In some examples, the similarity score model may be a cosine model. In other examples, the similarity score model may be a Hamming model. A similarity score may be calculated between the new embedding and one or more of the example embeddings. A K Nearest Neighbors (KNN) model may then be utilized to identify one or more intent types and/or skill types that correspond to the new embedding. In some examples, a nearest neighbor score value may be calculated for each of the one or more intent types and/or skill types based on application of the KNN model.” Here, Suwandy shows using a K Nearest Neighbors model, which is used to perform searches as understood in the art, to look for intents that correspond to the new embedding which in this instance is the client workspace as mentioned. The search for similar items is done using similarity scores and nearest neighbor score.
Neither Suwandy, Cohen, D’Alessandro, or Bravo-Marquez appears to teach “grouping utterances of client log data into clusters and mapping the clusters into items of the similar training workspace using the corresponding embeddings; recommending the first and second intents corresponding to the mapped items that exist in the similar training workspace and are absent from the given client workspace”.
However, Kumar in the same field of art teaches, “grouping utterances of client log data into clusters and mapping the clusters into items of the similar training workspace using the corresponding embeddings”
See Kumar in paragraph [0017] describing, “In some implementations, when preprocessing the text data with the one or more preprocessing techniques to generate the key intents, the transformation system may identify utterances in the text data, and may generate parts of speech tags for the text data (e.g., by assigning parts of speech tags that correspond to particular parts of speech, such as nouns, verbs, adjectives, adverbs, and/or the like).” Here, Kumar establishes utterances being a part of the text data which can be used to generate intents, in these, these utterances are grouped. Further, see Kumar in paragraph [0018] describing, “the transformation system 110 may convert the preprocessed data and the key intents into embeddings.” The intents mentioned can be turned into embeddings. Further, see Kumar in Figure 2,
PNG
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397
491
media_image2.png
Greyscale
. In this figure given by Kumar, we can see clusters of sets of observations which as mentioned before can be seen as training workspaces. The new observation, which also as mentioned can be seen as a client workspace consists of intents which come from utterances being mapped to other observations.
Further, Kumar teaches, “recommending the first and second intents corresponding to the mapped items that exist in the similar training workspace and are absent from the given client workspace.”
See Kumar in paragraph [0041], where it describes “In some implementations, the trained machine learning model 225 may classify (e.g., cluster) the new observation in a cluster, as shown by reference number 240. The observations within a cluster may have a threshold degree of similarity. As an example, if the machine learning system classifies the new observation in a first cluster (e.g., a preprocessed data cluster), then the machine learning system may provide a first recommendation.” Further, see Kumar in paragraph [0042] describing, “As another example, if the machine learning system were to classify the new observation in a second cluster (e.g., a key intents cluster), then the machine learning system may provide a second (e.g., different) recommendation” Here, for the new observation, as mentioned before being seen as a client workspace, if the recommendation is made using clusters instead of the new observation in itself it can be seen that the recommendation exist in a similar training workspace and is absent from the client workspace. Kumar here also teaches a first and second recommendation being made off intents.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the base reference of Suwandy, Cohen, D’Alessandro, and Bravo-Marquez into the methods of Freed by using Suwandy’s teachings of converting an intent into and embedding, comparing the embedding and training a model, with Cohen, D’Alessandro and Bravo-Marquez’s teachings of mapping items to a similar and incrementing a corresponding count of the similar, and using a second trained model to perform inferencing, and a created matrix with corresponding counts to items of a training workspace and further incorporate further Freed’s teachings of repeating operations to update workspaces of a matrix.
One of ordinary skill in the art would be motivated to do so because by integrating Freed’s frameworks into the methods of Suwandy, Cohen, D’Alessandro, and Bravo-Marquez, one with ordinary skill in the art would bring a “transformation system [that] generates a conversation summary from text data using a language transformation model” (Kumar, paragraph [0011]), “transformation system [that] may process the preprocessed data and the key intents, with a language model, to generate a conversation summary for the transcript. The transformation system may utilize the conversation summary to understand a customer journey, a customer issue, and/or a customer need, to provide an improved search experience, to identify novel categories, and/or the like” (Kumar, paragraph [0011]), and a “transformation system [that] may conserve computing resources, networking resources, and/or other resources that would have otherwise been consumed by failing to identify meaningful information and/or phrases in text data, failing to identify summaries of conversations provided in text data, being unable to utilize the meaningful information and/or phrases and the summaries of conversations, and/or the like.” (Kumar, paragraph [0011]).
Claim 19:
Regarding claim 19, Suwandy in view of Cohen, D’Alessandro and Bravo-Marquez teaches the limitations in claim 3.
Further, Suwandy teaches “The system of claim 14, the operations further comprising: mapping a given client workspace into the matrix”
See Suwandy in paragraph [0027] describing “During operation, the conversational computing service may process incoming natural language user inputs that have been received via conversational entities. When a new natural language input is received, that input may be encoded as an embedding. In some examples, the embedding may be compressed into a binary form. The embedding may then be added to the embedding library.” Here, Suwandy maps a client workspace into a matrix by taking in user inputs which can be seen as a client workspace, converting it into an embedding and then adding it to an embedding library, which previously is stated to already be perceived as a matrix, which can be seen as mapping into a matrix.
Further, Suwandy teaches, “conducting a search of the training workspace(s) that contain a similar set of items in the matrix compared to the given client workspace”
See Suwandy in paragraph [0027] where it describes “During operation, the conversational computing service may process incoming natural language user inputs that have been received via conversational entities. When a new natural language input is received, that input may be encoded as an embedding. In some examples, the embedding may be compressed into a binary form. The embedding may then be added to the embedding library. Once added to the embedding library, a similarity score model may be applied to the new embedding and one or more embeddings from the examples that were provided by the conversational entity developer. In some examples, the similarity score model may be a cosine model. In other examples, the similarity score model may be a Hamming model. A similarity score may be calculated between the new embedding and one or more of the example embeddings. A K Nearest Neighbors (KNN) model may then be utilized to identify one or more intent types and/or skill types that correspond to the new embedding. In some examples, a nearest neighbor score value may be calculated for each of the one or more intent types and/or skill types based on application of the KNN model.” Here, Suwandy shows using a K Nearest Neighbors model, which is used to perform searches as understood in the art, to look for intents that correspond to the new embedding which in this instance is the client workspace as mentioned. The search for similar items is done using similarity scores and nearest neighbor score.
Neither Suwandy, Cohen, D’Alessandro, or Bravo-Marquez appears to teach “recommending the first and second intents corresponding to the mapped items that exist in the similar training workspace and are absent from the given client workspace”.
However, Kumar in the same field of art teaches, “recommending the first and second intents corresponding to the mapped items that exist in the similar training workspace and are absent from the given client workspace”
See Kumar in paragraph [0017] describing, “In some implementations, when preprocessing the text data with the one or more preprocessing techniques to generate the key intents, the transformation system may identify utterances in the text data, and may generate parts of speech tags for the text data (e.g., by assigning parts of speech tags that correspond to particular parts of speech, such as nouns, verbs, adjectives, adverbs, and/or the like).” Here, Kumar establishes utterances being a part of the text data which can be used to generate intents, in these, these utterances are grouped. Further, see Kumar in paragraph [0018] describing, “the transformation system 110 may convert the preprocessed data and the key intents into embeddings.” The intents mentioned can be turned into embeddings. Further, see Kumar in Figure 2,
PNG
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397
491
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Greyscale
. In this figure given by Kumar, we can see clusters of sets of observations which as mentioned before can be seen as training workspaces. The ne observation, which also as mentioned can be seen as a client workspace consists of intents which come from utterances being mapped to other observations.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the base reference of Suwandy, Cohen, D’Alessandro, and Bravo-Marquez into the methods of Freed by using Suwandy’s teachings of converting an intent into and embedding, comparing the embedding and training a model, with Cohen, D’Alessandro and Bravo-Marquez’s teachings of mapping items to a similar and incrementing a corresponding count of the similar, and using a second trained model to perform inferencing, and a created matrix with corresponding counts to items of a training workspace and further incorporate further Freed’s teachings of repeating operations to update workspaces of a matrix.
One of ordinary skill in the art would be motivated to do so because by integrating Freed’s frameworks into the methods of Suwandy, Cohen, D’Alessandro, and Bravo-Marquez, one with ordinary skill in the art would bring a “transformation system [that] generates a conversation summary from text data using a language transformation model” (Kumar, paragraph [0011]), “transformation system [that] may process the preprocessed data and the key intents, with a language model, to generate a conversation summary for the transcript. The transformation system may utilize the conversation summary to understand a customer journey, a customer issue, and/or a customer need, to provide an improved search experience, to identify novel categories, and/or the like” (Kumar, paragraph [0011]), and a “transformation system [that] may conserve computing resources, networking resources, and/or other resources that would have otherwise been consumed by failing to identify meaningful information and/or phrases in text data, failing to identify summaries of conversations provided in text data, being unable to utilize the meaningful information and/or phrases and the summaries of conversations, and/or the like.” (Kumar, paragraph [0011]).
Claim 20:
Regarding claim 20, Suwandy in view of Cohen, D’Alessandro and Bravo-Marquez teaches the limitations in claim 14.
Neither Suwandy, Cohen, D’Alessandro, or Bravo-Marquez appears to teach the further limitations of this claim. However, Kumar in the same field of art teaches, “The system of claim 14, the operations further comprising: grouping utterances of client log data into clusters and mapping the clusters into items of the training workspace using the corresponding intent embeddings”
See Kumar in paragraph [0017] describing, “In some implementations, when preprocessing the text data with the one or more preprocessing techniques to generate the key intents, the transformation system may identify utterances in the text data, and may generate parts of speech tags for the text data (e.g., by assigning parts of speech tags that correspond to particular parts of speech, such as nouns, verbs, adjectives, adverbs, and/or the like).” Here, Kumar establishes utterances being a part of the text data which can be used to generate intents, in these, these utterances are grouped. Further, see Suwandy in paragraph [0018] describing, “the transformation system 110 may convert the preprocessed data and the key intents into embeddings.” The intents mentioned can be turned into embeddings. Further, see Kumar in Figure 2,
PNG
media_image2.png
397
491
media_image2.png
Greyscale
. In this figure given by Kumar, we can see clusters of sets of observations which as mentioned before can be seen as training workspaces. The new observation, which also as mentioned can be seen as a client workspace consists of intents which come from utterances being mapped to other observations.
Further, Kumar teaches, “recommending the first and second intents corresponding to the mapped items that exist in the training workspace.”
See Kumar in paragraph [0041], where it describes “In some implementations, the trained machine learning model 225 may classify (e.g., cluster) the new observation in a cluster, as shown by reference number 240. The observations within a cluster may have a threshold degree of similarity. As an example, if the machine learning system classifies the new observation in a first cluster (e.g., a preprocessed data cluster), then the machine learning system may provide a first recommendation.” Further, see Kumar in paragraph [0042] describing, “As another example, if the machine learning system were to classify the new observation in a second cluster (e.g., a key intents cluster), then the machine learning system may provide a second (e.g., different) recommendation” Here, for the new observation, as mentioned before being seen as a client workspace, if the recommendation is made using clusters instead of the new observation in itself it can be seen that the recommendation exist in a similar training workspace. Suwandy here also teaches a first and second recommendation being made off intents.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the base reference of Suwandy, Cohen, D’Alessandro, and Bravo-Marquez into the methods of Freed by using Suwandy’s teachings of converting an intent into and embedding, comparing the embedding and training a model, with Cohen, D’Alessandro and Bravo-Marquez’s teachings of mapping items to a similar and incrementing a corresponding count of the similar, and using a second trained model to perform inferencing, and a created matrix with corresponding counts to items of a training workspace and further incorporate further Freed’s teachings of repeating operations to update workspaces of a matrix.
One of ordinary skill in the art would be motivated to do so because by integrating Freed’s frameworks into the methods of Suwandy, Cohen, D’Alessandro, and Bravo-Marquez, one with ordinary skill in the art would bring a “transformation system [that] generates a conversation summary from text data using a language transformation model” (Kumar, paragraph [0011]), “transformation system [that] may process the preprocessed data and the key intents, with a language model, to generate a conversation summary for the transcript. The transformation system may utilize the conversation summary to understand a customer journey, a customer issue, and/or a customer need, to provide an improved search experience, to identify novel categories, and/or the like” (Kumar, paragraph [0011]), and a “transformation system [that] may conserve computing resources, networking resources, and/or other resources that would have otherwise been consumed by failing to identify meaningful information and/or phrases in text data, failing to identify summaries of conversations provided in text data, being unable to utilize the meaningful information and/or phrases and the summaries of conversations, and/or the like.” (Kumar, paragraph [0011]).
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
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/HASSAN RAMADAN SESAY/Examiner, Art Unit 2146
/USMAAN SAEED/Supervisory Patent Examiner, Art Unit 2146
