Abstract: A management server includes processing circuitry configured to accept a task that is a request for calculation by a distributed processing system from a terminal device, decompose the task into a plurality of jobs, determine, from calculation resources available for calculation servers obtained in advance from the calculation servers, respectively, a schedule of the jobs to be allocated to the calculation servers, transmit, based on the schedule, the jobs to be allocated to the calculation servers, respectively, accept calculation results of the jobs from the calculation servers, respectively, generate a calculation result of the task based on the accepted calculation results of the jobs, and transmit the calculation result of the task to the terminal device.

Abstract: An information processing apparatus including processing circuitry configured to accept a plurality of learned models selected by a user, and a generating step of generating an information processing process by combining the plurality of learned models accepted in the model accepting step.

Abstract: Sales management and video chat systems and methods use website listings that correspond to vehicles for sale at a place of business. A remote visitor clicks a “live video” button beside a desired consumer good, and schedules a time for a video chat, using their web browser. A salesperson receives an alert, via a sales application running on on the salesperson's mobile device. At the scheduled time, a video chat window opens in the visitor's web browser with live audio/video of the desired consumer good from the place of business in real-time. The website visitor uses their web browser for live communication, in real-time, to the salesperson on their sales application. After the video chat, the website visitor can use their browser to download and save a recording of the video chat session.

Abstract: An enriched Mobile Advertisement ID (MAID) database is built based on collected indoor positioning location detail records (LDRs). Indoor context is added into columns of each MAID entry in the enriched MAID database, providing enrichment and added value to the MAID database. An enrichment platform groups MAID entries with varying levels of location fidelity for sale by participants who have a vested interest in the creation of the enriched MAID database. An enrichment platform includes an enriched MAID database, which is built by an enrichment service. The enriched MAID database takes in location detail records, typically in a large batch, with mobile device location and timestamp associated with a given MAID. Each MAID row entry in the MAID database is annotated in additional database columns with indoor context, e.g., retail places or items that the mobile device dwelled on, and places that the mobile device walked past but didn't dwell.

Abstract: Vehicle sales management and video chat systems and methods use website listings that correspond to vehicles for sale at a dealership. A remote visitor clicks a “live video” button beside a desired vehicle, and schedules a time for a video chat, using their web browser. A dealership salesperson receives an alert, via a sales application running on on the salesperson's mobile device. At the scheduled time, a video chat window opens in the visitor's web browser with live audio/video of the desired vehicle from the dealership in real-time. The website visitor uses their web browser for live communication, in real-time, to the salesperson on their sales application. After the video chat, the website visitor can use their browser to download and save a recording of the video chat session.

Abstract: A location assignment system and method assigns and stores a location to each item in a retail system product line, across one or more store venues, that otherwise do not include location information, e.g., is not included in planogram data. Specific rules and algorithms assign a location for each product by inferring a location of each product lacking location information from other items that do have a location, and assigning the location of a most popularly sold item in a weighted grouping of given attributes of the item. Thus, given a complete catalog of every product sold in a given retail store system (which often runs in the tens of thousands or even hundreds of thousands of individual products—up to 40% of more of which have no location information within a planogram), the location assignment system assigns a location to every product sold.

Abstract: Shopper traffic flow is determined based on a filtered set of point of sale (POS) transaction data. A location analytics system determines a presumed best route taken by a customer upon entrance to the store, in walking between each item purchased, and then to the checkout stand. A determination is generated based on filtered parameters to provide visualization of shopper traffic flow in a retail location. Filtered parameters include shoppers for a specific item, or specific brand, to generate a determination as to where those particular shoppers dwelled and experienced impressions. The location analytics server generates visibility into spatial product relationships based on real, POS purchase data, and based on spatial layout data for a given store.

Abstract: Indoor positioning information provide location detail records (LDRs), which are visualized on a map to show a shopper's path through a given store. However, LDR often provides an incomplete picture of a shopper's true journey through a store. Thus, gaps in LDR data are augmented, or filled in, using a routing engine based on point of sale (POS) transaction data to connect the last known position from the first event to first known position from second event, and so on for all the LDR records. If the last LDR doesn't end at the checkout or exit, a final route to a checkout stand used is appended to the last LDR sequence signifying the end of the shopping journey. Dwells are determined based on position data from the LDRs of selected shoppers over a selected period of time. There might not be POS data, but if there is, the LDR data is augmented with positions as determined by the location of each product purchased—filling in all gaps with a routing service.

Abstract: A transaction based location assignment system creates a complete and more accurate location assignment database for all products available in a store. Transaction data from checkout stands (point of sale terminals) spread around a department store, and the location of each of those checkout stands during POS transactions, is used to create a most probable location of items available in the store. Memory matrixes are built comprising product purchase or transaction information from the checkout stands, and the location of each of the checkout stands, and weighting based on other factors, provide for a best fit match to determine the most probable location within the store of all items within the store. The transaction based location system has the capability to work both for items having a single location in the store, as well as for items available from multiple physical locations in the store.

Abstract: A computer system and set of routines with access to venue maps, merchandizing plans, and store catalogs for a merchant attempts to assign locations to items in the store catalog based on the merchandizing plan and map; when a location cannot be assigned to an item in a venue, a proxy location is determined based on categories associated with the item, a number of merchandising plans across the venues in which the item occurs, and/or clusters of items. An error is also determined in relation to the assigned location. The assigned location and error are output, for example, to guide people to the items.

Abstract: A location assignment system and method assigns and stores a location to each item in a retail system product line, across one or more store venues, that otherwise do not include location information, e.g., is not included in planogram data. Specific rules and algorithms assign a location for each product by inferring a location of each product lacking location information from other items that do have a location, and assigning the location of a most popularly sold item in a weighted grouping of given attributes of the item. Thus, given a complete catalog of every product sold in a given retail store system (which often runs in the tens of thousands or even hundreds of thousands of individual products—up to 40% of more of which have no location information within a planogram), the location assignment system assigns a location to every product sold.

Abstract: A computer system and set of routines with access to venue maps, merchandizing plans, and store catalogs for a merchant attempts to assign locations to items in the store catalog based on the merchandizing plan and map; when a location cannot be assigned to an item in a venue, a proxy location is determined based on categories associated with the item, a number of merchandising plans across the venues in which the item occurs, and/or clusters of items. An error is also determined in relation to the assigned location. The assigned location and error are output, for example, to guide people to the items.

Abstract: A computer system and set of routines with access to a map, a merchandizing plan, and a store catalog performs spatial matching of items to assign locations to the items; when spatial matching cannot be performed, group matching is attempted based on categories associated with the items. If group matching cannot be performed, the location of a department or of a store is assigned to the corresponding item. An error is also determined in relation to the assigned location. The assigned location and error are output, for example, to guide people to the items.

Abstract: While traveling to a destination, potentially traversing geo-fences along the way, a routine on a mobile device estimates arrival at the next geo-fence and performs a location check at the estimated arrival time. Between iterations of this process, the routine may correct the “time of next check” based on correction factors. When the mobile device arrives at the destination, it switches to terrestrial location determining services. In addition, the mobile device monitors its sensors to determine whether motion is gross motion which may warrant performing a location probe or whether motion is not gross motion, in which case location probes may be disabled.

Abstract: While traveling to a destination, potentially traversing geo-fences along the way, a routine on a mobile device estimates arrival at the next geo-fence and performs a location check at the estimated arrival time. Between iterations of this process, the routine may correct the “time of next check” based on correction factors. When the mobile device arrives at the destination, it switches to terrestrial location determining services. In addition, the mobile device monitors its sensors to determine whether motion is gross motion which may warrant performing a location probe or whether motion is not gross motion, in which case location probes may be disabled.

Abstract: Using ground truth events and sensors available on a first mobile device, an algorithm executed at the first mobile device determines the mobile device's position; the first mobile device also obtains wireless signal information; the wireless signal information is associated with the position determined via the sensor data and ground truth events; the associated data is stored and may be sent to or used by a second device; the second device may then, for example, detect then-current wireless signal information and may locate the second device by looking up the then-current wireless signal information in the associated data.

Abstract: A computer system and set of routines with access to venue maps, merchandizing plans, and store catalogs for a merchant attempts to assign locations to items in the store catalog based on the merchandizing plan and map; when a location cannot be assigned to an item in a venue, a proxy location is determined based on categories associated with the item, a number of merchandising plans across the venues in which the item occurs, and/or clusters of items. An error is also determined in relation to the assigned location. The assigned location and error are output, for example, to guide people to the items.

Abstract: A computer system and set of routines with access to a map, a merchandizing plan, and a store catalog performs spatial matching of items to assign locations to the items; when spatial matching cannot be performed, group matching is attempted based on categories associated with the items. If group matching cannot be performed, the location of a department or of a store is assigned to the corresponding item. An error is also determined in relation to the assigned location. The assigned location and error are output, for example, to guide people to the items.

Abstract: Tracking, and possibly rewarding or incentivizing those who drive traffic includes causing sharing actions via social networking or media platforms to register such actions with a tracking server. Sharing may be via sharing widgets, posting on Web pages, and/or copying and pasting URLs. The effectiveness of the tracking may be tracked, including over generations. Incentives may be based on effectiveness of sharing actions, for example number of clicks, or social and actual revenue generated. Incentives may include access through a pay wall Website or free or discounted access to content, products, or services.

Abstract: A system collects and correlates dynamic mobile user data location with a structure map and determined, for individuals and groups, average queue times for waypoints or average dwell time for areas within the structure. The determined times are made available for use.