Classifications

• • G—PHYSICS
  • G06—COMPUTING OR CALCULATING; COUNTING
  • G06F—ELECTRIC DIGITAL DATA PROCESSING
  • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
  • G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
  • G06F16/24—Querying
  • G06F16/245—Query processing
  • G06F16/2457—Query processing with adaptation to user needs
  • G06F16/24575—Query processing with adaptation to user needs using context
• • G—PHYSICS
  • G06—COMPUTING OR CALCULATING; COUNTING
  • G06F—ELECTRIC DIGITAL DATA PROCESSING
  • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
  • G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
  • G06F16/24—Querying
  • G06F16/242—Query formulation
  • G06F16/243—Natural language query formulation
• • G—PHYSICS
  • G06—COMPUTING OR CALCULATING; COUNTING
  • G06F—ELECTRIC DIGITAL DATA PROCESSING
  • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
  • G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
  • G06F16/24—Querying
  • G06F16/245—Query processing
  • G06F16/2453—Query optimisation
  • G06F16/24534—Query rewriting; Transformation
  • G06F16/24539—Query rewriting; Transformation using cached or materialised query results
• • G—PHYSICS
  • G06—COMPUTING OR CALCULATING; COUNTING
  • G06F—ELECTRIC DIGITAL DATA PROCESSING
  • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
  • G06F16/90—Details of database functions independent of the retrieved data types
  • G06F16/95—Retrieval from the web
  • G06F16/953—Querying, e.g. by the use of web search engines
  • G06F16/9535—Search customisation based on user profiles and personalisation
• • G—PHYSICS
  • G06—COMPUTING OR CALCULATING; COUNTING
  • G06F—ELECTRIC DIGITAL DATA PROCESSING
  • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
  • G06F16/90—Details of database functions independent of the retrieved data types
  • G06F16/95—Retrieval from the web
  • G06F16/953—Querying, e.g. by the use of web search engines
  • G06F16/9537—Spatial or temporal dependent retrieval, e.g. spatiotemporal queries
• • G—PHYSICS
  • G10—MUSICAL INSTRUMENTS; ACOUSTICS
  • G10L—SPEECH ANALYSIS TECHNIQUES OR SPEECH SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING TECHNIQUES; SPEECH OR AUDIO CODING OR DECODING
  • G10L15/00—Speech recognition
  • G10L15/08—Speech classification or search
  • G10L15/18—Speech classification or search using natural language modelling
• • G—PHYSICS
  • G10—MUSICAL INSTRUMENTS; ACOUSTICS
  • G10L—SPEECH ANALYSIS TECHNIQUES OR SPEECH SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING TECHNIQUES; SPEECH OR AUDIO CODING OR DECODING
  • G10L15/00—Speech recognition
  • G10L15/22—Procedures used during a speech recognition process, e.g. man-machine dialogue

Definitions

• loT Internet of things
• the loT allows objects to be sensed or controlled remotely across existing network infrastructure, creating opportunities for more direct integration of the physical world into computer-based systems, and resulting in improved efficiency, accuracy, and economic benefit in addition to reduced human intervention.
• loT has been applied to various industries including manufacturing, transportation, and agriculture. A growing portion of loT devices are created for consumer use. Examples of consumer applications include connected car, entertainment, residences and smart homes, wearable technology, quantified self, connected health, and smart retail. Consumer loT provides new opportunities for user experience and interfaces.
• Figure 2 is a flowchart showing a first embodiment method performed, at least in part, by the first embodiment system
• Figure 4A is a first screenshot generated by the first embodiment computers system.
• Figure 4B is a second screenshot generated by the first embodiment computers system.
• the computer readable storage medium can be a tangible device that can retain and store instructions for use by an instruction execution device.
• the computer readable storage medium may be, for example, but is not limited to, an electronic storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any suitable combination of the foregoing.
• a computer readable storage medium is not to be construed as being transitory signals per se, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through a waveguide or other transmission media (e.g., light pulses passing through a fiber optic cable), or electrical signals transmitted through a wire.
• Computer readable program instructions described herein can be downloaded to respective computing/processing devices from a computer readable storage medium or to an external computer or external storage device via a network, for example, the Internet, a local area network, a wide area network and/or a wireless network.
• the network may comprise copper transmission cables, optical transmission fibers, wireless transmission, routers, firewalls, switches, gateway computers and/or edge servers.
• a network adapter card or network interface in each computing/processing device receives computer readable program instructions from the network and forwards the computer readable program instructions for storage in a computer readable storage medium within the respective computing/processing device.
• the computer readable program instructions may also be loaded onto a computer, other
• communications fabric can be implemented, at least in part, with one or more buses.
• (i) is at least more persistent than a signal in transit; (ii) stores the program (including its soft logic and/or data) on a tangible medium (such as magnetic or optical domains); and (iii) is substantially less persistent than permanent storage.
• data storage may be more persistent and/or permanent than the type of storage provided by persistent storage device 210.
• Search context program 300 may include both substantive data (that is, the type of data stored in a database) and/or machine readable and performable instructions.
• persistent storage device 210 includes a magnetic hard disk drive.
• persistent storage device 210 may include a solid-state hard drive, a semiconductor storage device, a read-only memory (ROM), an erasable programmable read-only memory (EPROM), a flash memory, or any other computer readable storage media that is capable of storing program instructions or digital information.
• the media used by persistent storage device 210 may also be removable.
• a removable hard drive may be used for persistent storage device 210.
• Other examples include optical and magnetic disks, thumb drives, and smart cards that are inserted into a drive for transfer onto another computer readable storage medium that is also part of persistent storage device 210.
• the device correlation factor is relatively high (0.70) for the search result of BCD cars encyclopedia.
• the device correlation factor is relatively high (0.50) for the search result of BCD cars community forum. See FIG 4A.
• the confidence and relevance for the search results of BCD cars encyclopedia and cars community forum are high for the output from the loT television.
• the device correlation factors for the search results online encyclopedia article about BCD - a verb, BCD smartphone accessories, BCD clothing line, and BCD food company are low because there are low confidence and relevance for the search result in comparison to the loT output. If Abel had encountered a digital billboard advertising BCD clothing three weeks ago in the past, for example, the device correlation factor for BCD clothing may still be relatively low due to the time decay factor.
• processing proceeds to operation S262, where new search parameters determination mod 362 determines or generates new search parameters or terms for use in a second search query based on determined correlations.
• the device correlation factor determined in operation S256 and the user profile correlation factor determined in operation S260 are used as input to determine a cumulative correlation factor.
• Example cumulative correlation factor are shown in FIG. 4A.
• the cumulative correlation factor is determined by adding the device correlation factor to the user profile correlation factor.
• one factor is weighted in favor of another factor according to user preference and/or governing policy. If multiple devices are in range and produce similar device correlation factor values, the user profile correlation factor may have greater influence on the cumulative correlation factor.
• Some embodiments of the present invention determine the content of the output of a device by using image and audio processing techniques. Alternatively, some embodiments of the present invention determine the content of the output of a device by using metadata embedded in the data stream.
• An audio processing technique employed by some embodiments of the present invention is natural language processing.
• Figure 4B is screenshot 400b showing a second search conducted by the user with search context program 300 in the exemplary embodiment.
• user Abel conducts a search of BCD cars and search context program 300 generates new results.
• Search context program 300 generates hundreds of search results.
• a "set of items means there exists one or more items; there must exist at least one item, but there can also be two, three, or more items.
• a “subset of” items means there exists one or more items within a grouping of items that contain a common characteristic.
• a "plurality of” items means there exists at more than one item; there must exist at least two items, but there can also be three, four, or more items.
• “Includes” and any variants means, unless explicitly noted otherwise, “includes, but is not necessarily limited to.”

Landscapes

• Engineering & Computer Science (AREA)
• Databases & Information Systems (AREA)
• Theoretical Computer Science (AREA)
• Physics & Mathematics (AREA)
• General Engineering & Computer Science (AREA)
• General Physics & Mathematics (AREA)
• Data Mining & Analysis (AREA)
• Computational Linguistics (AREA)
• Health & Medical Sciences (AREA)
• Audiology, Speech & Language Pathology (AREA)
• Human Computer Interaction (AREA)
• Acoustics & Sound (AREA)
• Multimedia (AREA)
• Artificial Intelligence (AREA)
• Mathematical Physics (AREA)
• Information Retrieval, Db Structures And Fs Structures Therefor (AREA)

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Citations (5)

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• 2017
  • 2017-11-22 US US15/820,619 patent/US11200241B2/en active Active
• 2018
  • 2018-11-14 WO PCT/IB2018/058951 patent/WO2019102309A1/en not_active Ceased
  • 2018-11-14 JP JP2020526211A patent/JP7325156B2/ja active Active
  • 2018-11-14 DE DE112018005087.4T patent/DE112018005087T5/de active Pending
  • 2018-11-14 CN CN201880069426.9A patent/CN111279334A/zh active Pending
  • 2018-11-14 GB GB2007794.7A patent/GB2583203A/en not_active Withdrawn

Patent Citations (5)

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