# US 2020/0401594 A1 · Rescaling layer in neural network > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 2020/0401594 A1 - Document ID: US20200401594A1 - Status: Published application - Kind: A1 - Application number: 16/449,122 - Filed: 2019-06-21 - Published: 2020-12-24 - Earliest claimed: 2019-06-21 - Assignees: Microsoft Technology Licensing, LLC - Applicants: Microsoft Technology Licensing, LLC - Inventors: Daniel Sairom Krishnan Hewlett; Dan Liu; Qi Guo - Web page: https://patentgrep.com/patent/US20200401594A1 ## CPC - G06F 16/9532: Query formulation - G06F 16/24578: {using ranking} - G06F 17/15: Correlation function computation {including computation of convolution operations (arithmetic circuits for sum of products per se, e.g. multiply-accumulators G06F7/5443; digital filters, e.g. FIR, IIR, adaptive filters H03H17/00)} - G06F 17/18: for evaluating statistical data {, e.g. average values, frequency distributions, probability functions, regression analysis (forecasting specially adapted for a specific administrative, business or logistic context G06Q10/04)} - G06N 3/04: Architecture, e.g. interconnection topology - G06N 3/045: Combinations of networks - G06N 3/0464: Convolutional networks [CNN, ConvNet] - G06N 3/084: Backpropagation, e.g. using gradient descent - G06N 3/09: Supervised learning - G06Q 10/1053: Employment or hiring - G06Q 10/42: Determination of affinities or common interests between users - G06Q 10/48: using social graphs - G06F: ELECTRIC DIGITAL DATA PROCESSING (computer systems based on specific computational models G06N) - G06N: COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS - G06Q: INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR ## Abstract In an example embodiment, a platform is provided that utilizes information available to a computer system to feed a neural network. The neural network is trained to determine both the probability that a searcher would select a given potential search result if it was presented to him or her and the probability that a subject of the potential search result would respond to a communication from the searcher. These probabilities are combined to produce a single score that can be used to determine whether to present the searcher with the potential search result and, if so, how high to rank the potential search result among other search results. During the training process, a rescaling transformation for each input feature is learned and applied to the values for the input features. ## Claims ### Claim 1 A system for returning search results in an online computer system, the system comprising: a computer readable medium having instructions stored there on, which, when executed by a processor, cause the system to: retrieve a plurality of sample member profiles of members of an online service, a plurality of sample member labels, and activity and usage information pertaining to actions taken by those members on the online service; for each sample member profile in the plurality of sample member profiles: parse the sample member profile and the activity and usage information pertaining to actions taken by a member corresponding to the sample member profile on the online service to extract a first set of one or more features; parse an associated query to obtain one or more query features; feed sample member labels, the extracted first set of one or more features and the extracted one or more query features into a first machine learning algorithm to train a neural network to output a score indicative of a probability that a searcher will select a potential search result corresponding to the sample member profile and a probability that a member corresponding to the sample member profile will respond to a communication from a searcher, the neural network containing a rescaling layer, the rescaling layer learning rescaling transformations for one or more features during the training of the neural network. ### Claim 2 (depends on 1) The system of claim 1, wherein the instructions further cause the system to: obtain a plurality of candidate search results in response to a query corresponding to a searcher; for each candidate search result from the plurality of candidate search results: obtain activity and usage information for a member corresponding to the candidate search result; parse the candidate search result and the activity and usage information for the member corresponding to the candidate search result to extract a second set of one or more features; parse the query to obtain one or more query features; feed the extracted second set of one or more features and one or more query features into the neural network, outputting a score for the candidate search result; rank the plurality of candidate search results by their corresponding scores; and return one or more of the plurality of candidate search results based on the ranking. ### Claim 3 (depends on 1) The system of claim 1, wherein the neural network further includes an embedding layer that, during training of the neural network, adjusts one or more embeddings of values of features, each embedding indicating a representation of a value of a feature in a dimensional space. ### Claim 4 (depends on 3) The system of claim 3, wherein the embedding layer is designed to share embeddings across sets of features. ### Claim 5 (depends on 1) The system of claim 1, wherein the neural network further contains one or more hidden layers. ### Claim 6 (depends on 1) The system of claim 1, wherein the neural network is a deep neural network. ### Claim 7 (depends on 1) The system of claim 1, wherein the neural network is a convolutional neural network. ### Claim 8 A computer-implemented method for returning search results in an online computer system, the method comprising: retrieving a plurality of sample member profiles of members of an online service, a plurality of sample member labels, and activity and usage information pertaining to actions taken by those members on the online service; for each sample member profile in the plurality of sample member profiles: parsing the sample member profile and the activity and usage information pertaining to actions taken by a member corresponding to the sample member profile on the online service to extract a first set of one or more features; parsing an associated query to obtain one or more query features; feeding sample member labels, the extracted first set of one or more features and the extracted one or more query features into a first machine learning algorithm to train a neural network to output a score indicative of a probability that a searcher will select a potential search result corresponding to the sample member profile and a probability that a member corresponding to the sample member profile will respond to a communication from a searcher, the neural network containing a rescaling layer, the rescaling layer learning rescaling transformations for one or more features during the training of the neural network. ### Claim 9 (depends on 8) The method of claim 8, further comprising: obtaining a plurality of candidate search results in response to a query corresponding to a searcher; for each candidate search result from the plurality of candidate search results: obtaining activity and usage information for a member corresponding to the candidate search result; parsing the candidate search result and the activity and usage information for the member corresponding to the candidate search result to extract a second set of one or more features; parsing the query to obtain one or more query features; feeding the extracted second set of one or more features and one or more query features into the neural network, outputting a score for the candidate search result; ranking the plurality of candidate search results by their corresponding scores; and returning one or more of the plurality of candidate search results based on the ranking. ### Claim 10 (depends on 8) The method of claim 8, wherein the neural network further includes an embedding layer that, during training of the neural network, adjusts one or more embeddings of values of features, each embedding indicating a representation of a value of a feature in a dimensional space. ### Claim 11 (depends on 10) The method of claim 10, wherein the embedding layer is designed to share embeddings across sets of features. ### Claim 12 (depends on 8) The method of claim 8, wherein the neural network further contains one or more hidden layers. ### Claim 13 (depends on 8) The method of claim 8, wherein the neural network is a deep neural network. ### Claim 14 (depends on 8) The method of claim 8, wherein the neural network is a convolutional neural network. ### Claim 15 A non-transitory machine-readable storage medium comprising instructions, which when implemented by one or more machines, cause the one or more machines to perform operations comprising: retrieving a plurality of sample member profiles of members of an online service, a plurality of sample member labels, and activity and usage information pertaining to actions taken by those members on the online service; for each sample member profile in the plurality of sample member profiles: parsing the sample member profile and the activity and usage information pertaining to actions taken by a member corresponding to the sample member profile on the online service to extract a first set of one or more features; parsing an associated query to obtain one or more query features; feeding sample member labels, the extracted first set of one or more features and the extracted one or more query features into a first machine learning algorithm to train a neural network to output a score indicative of a probability that a searcher will select a potential search result corresponding to the sample member profile and a probability that a member corresponding to the sample member profile will respond to a communication from a searcher, the neural network containing a rescaling layer, the rescaling layer learning rescaling transformations for one or more features during the training of the neural network. ### Claim 16 (depends on 15) The non-transitory machine-readable storage medium of claim 15, further comprising: obtaining a plurality of candidate search results in response to a query corresponding to a searcher; for each candidate search result from the plurality of candidate search results: obtaining activity and usage information for a member corresponding to the candidate search result; parsing the candidate search result and the activity and usage information for the member corresponding to the candidate search result to extract a second set of one or more features; parsing the query to obtain one or more query features; feeding the extracted second set of one or more features and one or more query features into the neural network, outputting a score for the candidate search result; ranking the plurality of candidate search results by their corresponding scores; and returning one or more of the plurality of candidate search results based on the ranking. ### Claim 17 (depends on 15) The non-transitory machine-readable storage medium of claim 15, wherein the neural network further includes an embedding layer that, during training of the neural network, adjusts one or more embeddings of values of features, each embedding indicating a representation of a value of a feature in a dimensional space. ### Claim 18 (depends on 17) The non-transitory machine-readable storage medium of claim 17, wherein the embedding layer is designed to share embeddings across sets of features. ### Claim 19 (depends on 15) The non-transitory machine-readable storage medium of claim 15, wherein the neural network further contains one or more hidden layers. ### Claim 20 (depends on 15) The non-transitory machine-readable storage medium of claim 15, wherein the neural network is a deep neural network.