Abstract: Implementations described herein are directed to enabling collaborative ranking of interpretations of spoken utterances based on data that is available to an automated assistant and third-party agent(s), respectively. The automated assistant can determine first-party interpretation(s) of a spoken utterance provided by a user, and can cause the third-party agent(s) to determine third-party interpretation(s) of the spoken utterance provided by the user. In some implementations, the automated assistant can select a given interpretation, from the first-party interpretation(s) and the third-party interpretation(s), of the spoken utterance, and can cause a given third-party agent to satisfy the spoken utterance based on the given interpretation.

Abstract: Implementations described herein utilize an independent server for facilitating secure exchange of data between multiple disparate parties. The independent server receives client data, via an automated assistant application executing at least in part at a client device, that is to be transmitted to a given third-party application. The independent server processes the client data, using a first encoder-decoder model, to generate opaque client data, and transmits the opaque client data to the given third-party application and without transmitting any of the client data. Further, the independent server receives response data, via the given third-party application, that is generated based on the opaque client data and that is to be transmitted back to the client device. The independent server processes the response data, using a second encoder-decoder model, to generate opaque response data, and transmits the opaque response data to the client device and without transmitting any of the response data.

Abstract: In the disclosed systems and methods for categorizing medical data, a computer system obtains, in electronic form, a plurality of medical records. Each medical record includes corresponding medical data from a respective medical evaluation and corresponding metadata comprising a plurality of attributes about the respective medical evaluation. Each respective attribute comprises a corresponding string of text. The computer system determines, for each respective pair of medical records consisting of a first medical record and a second medical record, a corresponding pairwise similarity between, for each respective attribute in a set of attributes, the corresponding string of text for the first medical record and the corresponding string of text for the second medical record. The computer system identifies a first subset of the plurality of medical records. Each respective medical record in the first subset is connected to each other through pairwise similarities that each satisfies a similarity threshold.

Abstract: A computer-based credit evaluation system is described that uses a machine learning-based credit risk model with an adverse action methodology to assess applicant credit profiles and identify adverse action factors for credit request denials. The credit risk model is trained to assess an applicant's credit profile based on characteristics. In the case of a denial, the system compares applicant values of the characteristics against anchor values for the characteristics determined based on values from a top scoring credit profile. The system uses the credit risk model to calculate a replacement score for each of the characteristics by replacing the applicant value for the characteristic with an anchor value for the characteristic. The system ranks the characteristics based on the replacement scores, and identifies the top ranked characteristics as the adverse action factors for the denial.

Abstract: Implementations described herein are directed to enabling collaborative ranking of interpretations of spoken utterances based on data that is available to an automated assistant and third-party agent(s), respectively. The automated assistant can determine first-party interpretation(s) of a spoken utterance provided by a user, and can cause the third-party agent(s) to determine third-party interpretation(s) of the spoken utterance provided by the user. In some implementations, the automated assistant can select a given interpretation, from the first-party interpretation(s) and the third-party interpretation(s), of the spoken utterance, and can cause a given third-party agent to satisfy the spoken utterance based on the given interpretation.

Abstract: Implementations described herein are directed to enabling collaborative ranking of interpretations of spoken utterances based on data that is available to an automated assistant and third-party agent(s), respectively. The automated assistant can determine first-party interpretation(s) of a spoken utterance provided by a user, and can cause the third-party agent(s) to determine third-party interpretation(s) of the spoken utterance provided by the user. In some implementations, the automated assistant can select a given interpretation, from the first-party interpretation(s) and the third-party interpretation(s), of the spoken utterance, and can cause a given third-party agent to satisfy the spoken utterance based on the given interpretation.

Abstract: Implementations described herein utilize an independent server for facilitating secure exchange of data between multiple disparate parties. The independent server receives client data, via an automated assistant application executing at least in part at a client device, that is to be transmitted to a given third-party application. The independent server processes the client data, using a first encoder-decoder model, to generate opaque client data, and transmits the opaque client data to the given third-party application and without transmitting any of the client data. Further, the independent server receives response data, via the given third-party application, that is generated based on the opaque client data and that is to be transmitted back to the client device. The independent server processes the response data, using a second encoder-decoder model, to generate opaque response data, and transmits the opaque response data to the client device and without transmitting any of the response data.

Abstract: Implementations described herein are directed to enabling collaborative ranking of interpretations of spoken utterances based on data that is available to an automated assistant and third-party agent(s), respectively. The automated assistant can determine first-party interpretation(s) of a spoken utterance provided by a user, and can cause the third-party agent(s) to determine third-party interpretation(s) of the spoken utterance provided by the user. In some implementations, the automated assistant can select a given interpretation, from the first-party interpretation(s) and the third-party interpretation(s), of the spoken utterance, and can cause a given third-party agent to satisfy the spoken utterance based on the given interpretation.

Abstract: A computer-based credit evaluation system is described that uses a machine learning-based credit risk model with an adverse action methodology to assess applicant credit profiles and identify adverse action factors for credit request denials. The credit risk model is trained to assess an applicant's credit profile based on characteristics. In the case of a denial, the system compares applicant values of the characteristics against anchor values for the characteristics determined based on values from a top scoring credit profile. The system uses the credit risk model to calculate a replacement score for each of the characteristics by replacing the applicant value for the characteristic with an anchor value for the characteristic. The system ranks the characteristics based on the replacement scores, and identifies the top ranked characteristics as the adverse action factors for the denial.

Abstract: The present invention relates to novel semi-synthetic meningococcal conjugate vaccine comprising novel synthetic oligosaccharide conjugated to a carrier protein. The present invention also relates to novel synthetic meningococcal oligosaccharide and a process for its preparation.

Abstract: The present invention relates to a preparation method which is performed by expressing the recombinant carrier proteins in Escherichia coli and purification thereof. More particular, the invention relates to industrially scalable process for the recovery of recombinant carrier proteins.

Abstract: The technology disclosed allows for remote distributed computing and storage of vehicle data obtained from one or more vehicles. Specifically, the technology disclosed is capable of determining, at a vehicle, resource requirements for a software application. The technology disclosed is also capable of selecting a computation resource from a plurality of computation resources based on one or more selection policies for meeting the resource requirements, sending the vehicle data from the vehicle to the selected computation resource, receiving at the vehicle data from the selected computation resource and utilizing the data obtained from the computation resource and data in the vehicle in the software application.

Abstract: A method for a shared resource management system is discussed. The method includes managing a shareable digital artifact for sharing between applications hosted by a user devices. The shared artifact is accessible, at the same time, by first and second application. The use of the shared artifact by the first application makes the shared artifact unavailable for use by the second application. The method includes determining whether a notification is received from the first application indicating that the shared artifact is used by the first application at a merchant. The method includes, in response to a determination that the notification is received, marking the shared artifact used. In response to marking the shared artifact used, a used artifact notification is sent to the second application to indicate that the shared artifact has been used and it is unavailable for use by the second application.

Abstract: The technology disclosed allows for remote distributed computing and storage of data obtained from one or more client devices. Specifically, the technology disclosed is capable of receiving, at the client device, data from at least one data source. The technology discloses is also capable of determining, at the client device, data information associated with the data from the at least one data source, selecting a computation resource from a plurality of computation resources based on one or more selection policies, where the selection policies are associated with one or more of location constraints, time constraints, and data type constraints and sending the data from the client device to the selected computation resource for processing.

Abstract: The present invention relates to novel semi-synthetic meningococcal conjugate vaccine comprising novel synthetic oligosaccharide conjugated to a carrier protein. The present invention also relates to novel synthetic meningococcal oligosaccharide and a process for its preparation.

Abstract: Embodiments generally relate to distributed computing of vehicle data. In some embodiments, a method includes determining, at a vehicle, resource requirements for a software application. The method further includes selecting a computation resource from a plurality of computation resources based on one or more selection policies for meeting the resource requirements. The method further includes sending the vehicle data from the vehicle to the selected computation resource. The method further includes receiving at the vehicle data from the selected computation resource. The method further includes utilizing the data obtained from the computation resource and data in the vehicle in the software application.

Abstract: Embodiments generally relate to distributed computing of data. In some embodiments, a method includes receiving, at a client device, data from at least one data source. The method further includes determining, at the client device, data information associated with the data from the at least one data source. The method further includes selecting a computation resource from a plurality of computation resources based on one or more selection policies, where the selection policies are associated with one or more of location constraints, time constraints, and data type constraints. The method further includes sending the data from the client device to the selected computation resource for processing.

Abstract: The present invention relates to high level expression of bacterial toxoid or toxin protein of pharmacological interest by means of an optimized novel polynucleotide sequence and host transformed with the said polynucleotide. Specifically, the invention provides a method for high production of polypeptide CRM197 wherein, the polynucleotide of the invention is used to transform a suitable host resulting in over-expression of corresponding proteins and a method for isolating the expressed polypeptide. More particularly, the present invention relates to high level expression of CRM197 in Escherichia coli and a method for the isolation and purification thereof.

Abstract: The present invention relates to a preparation method which is performed by expressing the recombinant carrier proteins in Escherichia coli and purification thereof. More particular, the invention relates to industrially scalable process for the recovery of recombinant carrier proteins.

Abstract: The present invention relates to high level expression of bacterial toxoid or toxin protein of pharmacological interest by means of an optimized novel polynucleotide sequence and host transformed with the said polynucleotide. Specifically, the invention provides a method for high production of polypeptide CRM197 wherein, the polynucleotide of the invention is used to transform a suitable host resulting in over-expression of corresponding proteins and a method for isolating the expressed polypeptide. More particularly, the present invention relates to high level expression of CRM197 in Escherichia coli and a method for the isolation and purification thereof.