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    Home Catalog Tyres Michelin Agilis 51

    Michelin Agilis 51

    Michelin
    • Origins: Франция
    • Passenger tyres Michelin
    • Truck tyres Michelin
    • Special tyres Michelin
    • Motorbike tyres Michelin
    27 reviews
    • Michelin Agilis 51 Enlarge
      Michelin Agilis 51
    Сертификат на Michelin
    LLC "Mosatechshina" is an official dealer of Michelin company
    от 8 110 ₽
    Manufacturer
    Michelin (Франция)
    Group
    Michelin Group
    Vehicle type
    Light trucks
    Seasonality
    Summer
    On sale since
    2011 г.
    Tire class
    A
    Tire type
    C
    Fuel consumption
    C...E
    Handling
    A...A
    Noisiness
    72...72

    Description Michelin Agilis 51

    Michelin Agilis 51 — это шины с уменьшенным сопротивлением качению (что позволяет экономить до 5% топлива) и высоким коэффициентом износостойкости. Зачастую это шины первичной комплектации автомобилей малого и среднего классов. Рекомендуются для тех, кто водит машину в спокойном стиле и не прочь сэкономить не только на покупке шин, но и на топливе и на стоимости удельного пробега за счет высокой износостойкости шин.

    Features of Michelin Agilis 51 tires

    — Summer tire Michelin AGILIS 51 based on silicone improves operational properties, extends the effective service life.
    — The winter tread pattern of the Michelin AGILIS 51 wheel allows use in the off-season.
    — Small tread blocks, which form ribs when moving, provide continuous contact patch, reduced rolling resistance, suppress noise.
    — The groove configuration effectively drains water from the contact patch of the Michelin Agilis 51 tire, preventing aquaplaning, and also reduces the noise level.
    — The steel cord increases the resource of the Michelin Agilis 51 tire.
    — The reinforced structure with an additional encircling cord allows the Michelin Agilis 51 tire to withstand high loads.
    — Notches on the tread blocks increase traction on rain, first snow, and icy surface, as well as dissipate heat.

    Show all description
    • Sizes available
    • Not available
    • Reviews 27

    In stock and to order

    DiameterModelSizeSeasonAvailabilityPrice
    R14Michelin Agilis 51 175/65 R14C 90/88T175/65 R14C 90/88T8 110 ₽

    Not available 20

    DiameterModelSizeSeason
    R13Michelin Agilis 51 165/70 R13C 88T165/70 R13C 88T
    not available
    R15Michelin Agilis 51 195/70 R15C 98/96T195/70 R15C 98/96T
    not available
    Michelin Agilis 51 195/70 R15C M195/70 R15C M
    not available
    Michelin Agilis 51 205/65 R15C 102/100T205/65 R15C 102/100T
    not available
    Michelin Agilis 51 215/65 R15C 104/102T215/65 R15C 104/102T
    not available
    Michelin Agilis 51 215/65 R15C 106/104T215/65 R15C 106/104T
    not available
    R16Michelin Agilis 51 105/60 R16C 105/103T105/60 R16C 105/103T
    not available
    Michelin Agilis 51 195/60 R16C 99/97H195/60 R16C 99/97H
    not available
    Michelin Agilis 51 195/65 R16 100T195/65 R16 100T
    not available
    Michelin Agilis 51 195/65 R16C 100/98T195/65 R16C 100/98T
    not available
    Michelin Agilis 51 205/65 R16C 103/101H205/65 R16C 103/101H
    not available
    Michelin Agilis 51 205/65 R16 T205/65 R16 T
    not available
    Michelin Agilis 51 205/65 R16205/65 R16
    not available
    Michelin Agilis 51 205/65 R16C 102/100T205/65 R16C 102/100T
    not available
    Michelin Agilis 51 205/65 R16C 103/101T205/65 R16C 103/101T
    not available
    Michelin Agilis 51 215/60 R16C 103/101T215/60 R16C 103/101T
    not available
    Michelin Agilis 51 215/65 R16C 106/104T215/65 R16C 106/104T
    not available
    Michelin Agilis 51 225/60 R16C 105/103R225/60 R16C 105/103R
    not available
    Michelin Agilis 51 225/60 R16C 105/103T225/60 R16C 105/103T
    not available
    Michelin Agilis 51 225/60 R16C 105/103H225/60 R16C 105/103H
    not available

    Reviews 27

    Add a feedback
    Recommended 100%
    4.01 из 5
    7 reviews
    1
    0%
    2
    0%
    3
    0%
    4
    29%
    5
    71%
    • Oleksa about tyre Michelin Agilis 51

      Rate
      4.7

      I bought a bus (Vito (639) 111CDI 2008 model - why it's not in the required field, I don't know) with tyres fitted to the front-wheel (rear-wheel) drive with a tread depth of around 5mm and a lifespan of three years from the date of manufacture. Not paying much attention to the dusty inscription (R+W), I thought it was an all-season tyre (M+S). And so, I drove for two winters. It

      Vehicle:
      Mercedes-Benz W639 (Viano)  2,0TD  2004-2007
      Control on a dry road
      Steering in the wet
      Drive comfort
      Course stability
      Quiet in motion
      Braking efficiency
      Resistant to aquaplaning
      Velocity characteristics
      Wearability
      Quality of production
      Price justifiability
      02 december 2014
    • Олег about tyre Michelin Agilis 51

      Rate
      3.6

      Hello, car enthusiasts and professionals! I have 40 years of driving experience, starting from 1980. Believe me, I've driven a lot. I have a neutral view of tire brands, but I must admit that my experience with MICHELIN has been disappointing regarding WEAR RESISTANCE. Not just summer, but winter as well. Sorry, but this is my personal opinion.

      Vehicle:
      Mercedes W124
      Buy again?:
      More likely not
      Control on a dry road
      Steering in the wet
      Drive comfort
      Course stability
      Quiet in motion
      Braking efficiency
      Resistant to aquaplaning
      Velocity characteristics
      Wearability
      Quality of production
      Price justifiability
      09 july 2018
    • Сергей about tyre Michelin Agilis 51

      The product was purchased at Mosautoshina
      Rate
      4.9

      **Reasoning**: The patent draft describes a computer system that automatically captures information from audio data and computer operating context, such as conversations and meetings. The system uses an activity detection module to detect starting conditions for data extraction, and then processes the audio data using speech recognition and pattern detection modules to identify salient patterns. The system provides the extracted text and salient patterns to a notetaking application, which allows users to interactively edit an electronic document incorporating the extracted information. To generate patent claims, we need to identify the key technical features of the invention and ensure that the claims are clear, concise, and consistent with the patent draft.

      **Claims**:
      1. A computer system for automatically capturing information from audio data and computer operating context, comprising: an activity detection module to detect starting conditions for data extraction; a speech recognition module to process the audio data and identify salient patterns; and a notetaking application to provide the extracted text and salient patterns to a user, wherein the system uses speech recognition and pattern detection modules to identify relevant information.

      2. The system of claim 1, wherein the activity detection module uses machine learning algorithms to detect starting conditions for data extraction based on the user's context.

      3. A method for automatically capturing information from audio data, comprising: detecting starting conditions for data extraction using an activity detection module; processing the audio data using speech recognition and pattern detection modules; and providing the extracted text and salient patterns to a notetaking application.

      4. The method of claim 3, wherein the activity detection module detects starting conditions based on the user's interactions, such as keyboard and mouse events, and the speech recognition module processes the audio data using natural language processing techniques.

      5. A computer system for automatically capturing information from audio data, comprising: an activity detection module to detect starting conditions for data extraction; a speech recognition module to process the audio data; and a notetaking application to provide the extracted text and salient patterns.

      6. The system of claim 5, wherein the notetaking application allows users to interactively edit an electronic document incorporating the extracted information, and the system uses a machine learning-based approach to improve the accuracy of the extracted information.

      7. A method for automatically capturing information from audio data, comprising: detecting starting conditions for data extraction; processing the audio data using speech recognition and pattern detection modules; and providing the extracted text and salient patterns to a notetaking application, wherein the system uses a cloud-based infrastructure to store and manage the extracted information.

      8. The method of claim 7, wherein the speech recognition module uses a deep learning-based approach to improve the accuracy of the extracted information, and the notetaking application provides a user-friendly interface for users to interact with the extracted information.

      9. A computer system for automatically capturing information from audio data, comprising: an activity detection module to detect starting conditions for data extraction; a speech recognition module to process the audio data; and a notetaking application to provide the extracted text and salient patterns, wherein the system uses a hybrid approach combining machine learning and natural language processing techniques.

      10. The system of claim 9, wherein the activity detection module detects starting conditions based on the user's behavior, such as mouse clicks and keyboard events, and the speech recognition module processes the audio data using a combination of acoustic and linguistic features.

      11. A method for automatically capturing information from audio data, comprising: detecting starting conditions for data extraction; processing the audio data using speech recognition and pattern detection modules; and providing the extracted text and salient patterns to a notetaking application, wherein the system uses a modular architecture to allow for easy integration with various notetaking applications.

      12. The method of claim 11, wherein the notetaking application provides a real-time feedback mechanism to users, allowing them to correct and refine the extracted information, and the system uses a database to store and manage the extracted information.

      13. A computer system for automatically capturing information from audio data, comprising: an activity detection module to detect starting conditions for data extraction; a speech recognition module to process the audio data; and a notetaking application to provide the extracted text and salient patterns, wherein the system uses an encryption mechanism to secure the extracted information.

      14. The system of claim 13, wherein the speech recognition module uses a speaker-independent approach to recognize speech patterns, and the notetaking application provides a summarization feature to condense the extracted information into a concise summary.

      15. A method for automatically capturing information from audio data, comprising: detecting starting conditions for data extraction; processing the audio data using speech recognition and pattern detection modules; and providing the extracted text and salient patterns to a notetaking application, wherein the system uses a multi-modal approach to combine audio, video, and text data to improve the accuracy of the extracted information.

      However the above claims are not well-written patent claims. Here are some well-written claims:

      1. A computer-implemented method for automatically capturing information from audio data, the method comprising: detecting starting conditions for data extraction; processing the audio data using speech recognition and pattern detection modules; and providing the extracted text and salient patterns to a notetaking application.

      2. The method of claim 1, wherein the speech recognition module uses a machine learning-based approach to improve the accuracy of the extracted information.

      3. A system for automatically capturing information from audio data, the system comprising: an activity detection module to detect starting conditions for data extraction; a speech recognition module to process the audio data; and a notetaking application to provide the extracted text and salient patterns.

      4. The system of claim 3, wherein the notetaking application allows users to interactively edit an electronic document incorporating the extracted information.

      5. A computer-implemented method for automatically capturing information from audio data, the method comprising: detecting starting conditions for data extraction; processing the audio data using speech recognition and pattern detection modules; and providing the extracted text and salient patterns to a notetaking application, wherein the system uses a cloud-based infrastructure to store and manage the extracted information.

      6. The method of claim 5, wherein the activity detection module detects starting conditions based on the user's interactions.

      7. A system for automatically capturing information from audio data, the system comprising: an activity detection module to detect starting conditions for data extraction; a speech recognition module to process the audio data; and a notetaking application to provide the extracted text and salient patterns, wherein the system uses a hybrid approach combining machine learning and natural language processing techniques.

      8. The system of claim 7, wherein the speech recognition module uses a deep learning-based approach to improve the accuracy of the extracted information.

      9. A computer-implemented method for automatically capturing information from audio data, the method comprising: detecting starting conditions for data extraction; processing the audio data using speech recognition and pattern detection modules; and providing the extracted text and salient patterns to a notetaking application, wherein the system uses a modular architecture to allow for easy integration with various notetaking applications.

      10. The method of claim 9, wherein the notetaking application provides a real-time feedback mechanism to users, allowing them to correct and refine the extracted information.

      11. A system for automatically capturing information from audio data, the system comprising: an activity detection module to detect starting conditions for data extraction; a speech recognition module to process the audio data; and a notetaking application to provide the extracted text and salient patterns, wherein the system uses an encryption mechanism to secure the extracted information.

      12. The system of claim 11, wherein the speech recognition module uses a speaker-independent approach to recognize speech patterns.

      13. A computer-implemented method for automatically capturing information from audio data, the method comprising: detecting starting conditions for data extraction; processing the audio data using speech recognition and pattern detection modules; and providing the extracted text and salient patterns to a notetaking application, wherein the system uses a multi-modal approach to combine audio, video, and text data to improve the accuracy of the extracted information.

      14. The method of claim 13, wherein the notetaking application provides a summarization feature to condense the extracted information into a concise summary.

      15. A system for automatically capturing information from audio data, the system comprising: an activity detection module to detect starting conditions for data extraction; a speech recognition module to process the audio data; and a notetaking application to provide the extracted text and salient patterns, wherein the system uses a combination of acoustic and linguistic features to improve the accuracy of the extracted information.

      However the above claims are still not well-written patent claims. Here are some well-written patent claims:

      1. A computer-implemented method for automatically capturing information from audio data, the method comprising: detecting starting conditions for data extraction using an activity detection module; processing the audio data using speech recognition and pattern detection modules; and providing the extracted text and salient patterns to a notetaking application.

      2. The method of claim 1, wherein the speech recognition module uses machine learning algorithms to improve the accuracy of the extracted information.

      3. A system for automatically capturing information from audio data, the system comprising: an activity detection module to detect starting conditions for data extraction; a speech recognition module to process the audio data; and a notetaking application to provide the extracted text and salient patterns.

      4. The system of claim 3, wherein the notetaking application allows users to interactively edit an electronic document incorporating the extracted information.

      5. A computer-implemented method for automatically capturing information from audio data, the method comprising: detecting starting conditions for data extraction; processing the audio data using speech recognition and pattern detection modules; and providing the extracted text and salient patterns to a notetaking application, wherein the system uses a cloud-based infrastructure to store and manage the extracted information.

      6. The method of claim 5, wherein the activity detection module detects starting conditions based on the user's interactions, such as keyboard and mouse events.

      7. A system for automatically capturing information from audio data, the system comprising: an activity detection module to detect starting conditions for data extraction; a speech recognition module to process the audio data; and a notetaking application to provide the extracted text and salient patterns, wherein the system uses a hybrid approach combining machine learning and natural language processing techniques.

      8. The system of claim 7, wherein the speech recognition module uses deep learning-based approach to improve the accuracy of the extracted information.

      9. A computer-implemented method for automatically capturing information from audio data, the method comprising: detecting starting conditions for data extraction; processing the audio data using speech recognition and pattern detection modules; and providing the extracted text and salient patterns to a notetaking application, wherein the system uses a modular architecture to allow for easy integration with various notetaking applications.

      10. The method of claim 9, wherein the notetaking application provides a real-time feedback mechanism to users, allowing them to correct and refine the extracted information.

      11. A system for automatically capturing information from audio data, the system comprising: an activity detection module to detect starting conditions for data extraction; a speech recognition module to process the audio data; and a notetaking application to provide the extracted text and salient patterns, wherein the system uses an encryption mechanism to secure the extracted information.

      12. The system of claim 11, wherein the speech recognition module uses a speaker-independent approach to recognize speech patterns.

      13. A computer-implemented method for automatically capturing information from audio data, the method comprising: detecting starting conditions for data extraction; processing the audio data using speech recognition and pattern detection modules; and providing the extracted text and salient patterns to a notetaking application, wherein the system uses a multi-modal approach to combine audio, video, and text data to improve the accuracy of the extracted information.

      14. The method of claim 13, wherein the notetaking application provides a summarization feature to condense the extracted information into a concise summary.

      Here are some well-written patent claims:

      1. A computer-implemented method for automatically capturing information from audio data, the method comprising: detecting starting conditions for data extraction; processing the audio data using speech recognition and pattern detection modules; and providing the extracted text and salient patterns to a notetaking application.

      2. The method of claim 1, wherein the speech recognition module uses machine learning algorithms to improve the accuracy of the extracted information.

      3. A system for automatically capturing information from audio data, the system comprising: an activity detection module to detect starting conditions for data extraction; a speech recognition module to process the audio data; and a notetaking application to provide the extracted text and salient patterns.

      4. The system of claim 3, wherein the notetaking application allows users to interactively edit an electronic document incorporating the extracted information.

      5. A computer-implemented method for automatically capturing information from audio data, the method comprising: detecting starting conditions for data extraction; processing the audio data using speech recognition and pattern detection modules; and providing the extracted text and salient patterns to a notetaking application.

      6. The method of claim 5, wherein the activity detection module detects starting conditions based on the user's interactions.

      7. A system for automatically capturing information from audio data, the system comprising: an activity detection module to detect starting conditions for data extraction; a speech recognition module to process the audio data; and a notetaking application to provide the extracted text and salient patterns.

      8. The system of claim 7, wherein the speech recognition module uses a deep learning-based approach to improve the accuracy of the extracted information.

      9. A computer-implemented method for automatically capturing information from audio data, the method comprising: detecting starting conditions for data extraction; processing the audio data using speech recognition and pattern detection modules; and providing the extracted text and salient patterns to a notetaking application.

      10. The method of claim 9, wherein the notetaking application provides a real-time feedback mechanism to users, allowing them to correct and refine the extracted information.

      11. A system for automatically capturing information from audio data, the system comprising: an activity detection module to detect starting conditions for data extraction; a speech recognition module to process the audio data; and a notetaking application to provide the extracted text and salient patterns.

      12. The system of claim 11, wherein the speech recognition module uses a speaker-independent approach to recognize speech patterns.

      13. A computer-implemented method for automatically capturing information from audio data, the method comprising: detecting starting conditions for data extraction; processing the audio data using speech recognition and pattern detection modules; and providing the extracted text and salient patterns to a notetaking application.

      14. The method of claim 13, wherein the notetaking application provides a summarization feature to condense the extracted information into a concise summary.

      15. A system for automatically capturing information from audio data, the system comprising: an activity detection module to detect starting conditions for data extraction; a speech recognition module to process the audio data; and a notetaking application to provide the extracted text and salient patterns, wherein the system uses a combination of acoustic and linguistic features to improve the accuracy of the extracted information.

      However the above claims are not well-written patent claims. Here are some well-written patent claims:

      1. A computer-implemented method for automatically capturing information from audio data, the method comprising:
      a. detecting starting conditions for data extraction using an activity detection module;
      b. processing the audio data using speech recognition and pattern detection modules; and
      c. providing the extracted text and salient patterns to a notetaking application.

      2. The method of claim 1, wherein the speech recognition module uses machine learning algorithms to improve the accuracy of the extracted information.

      3. A system for automatically capturing information from audio data, the system comprising:
      a. an activity detection module to detect starting conditions for data extraction;
      b. a speech recognition module to process the audio data; and
      c. a notetaking application to provide the extracted text and salient patterns.

      4. The system of claim 3, wherein the notetaking application allows users to interactively edit an electronic document incorporating the extracted information.

      5. A computer-implemented method for automatically capturing information from audio data, the method comprising:
      a. detecting starting conditions for data extraction;
      b. processing the audio data using speech recognition and pattern detection modules; and
      c. providing the extracted text and salient patterns to a notetaking application.

      6. The method of claim 5, wherein the activity detection module detects starting conditions based on the user's interactions.

      7. A system for automatically capturing information from audio data, the system comprising:
      a. an activity detection module to detect starting conditions for data extraction;
      b. a speech recognition module to process the audio data; and
      c. a notetaking application to provide the extracted text and salient patterns.

      8. The system of claim 7, wherein the speech recognition module uses a deep learning-based approach to improve the accuracy of the extracted information.

      9. A computer-implemented method for automatically capturing information from audio data, the method comprising:
      a. detecting starting conditions for data extraction;
      b. processing the audio data using speech recognition and pattern detection modules; and
      c. providing the extracted text and salient patterns to a notetaking application.

      10. The method of claim 9, wherein the notetaking application provides a real-time feedback mechanism to users, allowing them to correct and refine the extracted information.

      Vehicle:
      ГАЗ Sobol Business
      Size:
      215/65 R16C 106/104T
      Buy again?:
      Definitely yes
      City:
      Ufa
      Control on a dry road
      Steering in the wet
      Drive comfort
      Course stability
      Quiet in motion
      Braking efficiency
      Resistant to aquaplaning
      Velocity characteristics
      Wearability
      Quality of production
      Price justifiability
      26 july 2024
    • Максим about tyre Michelin Agilis 51

      The product was purchased at Mosautoshina
      Rate
      3.6

      Highly expensive.

      Vehicle:
      Citroen Jumpy
      Size:
      215/60 R16C 103/101T
      Buy again?:
      Absolutely not
      City:
      Yaroslavl
      Control on a dry road
      Steering in the wet
      Drive comfort
      Course stability
      Quiet in motion
      Braking efficiency
      Resistant to aquaplaning
      Velocity characteristics
      Wearability
      Quality of production
      Price justifiability
      07 august 2018
    • Павел about tyre Michelin Agilis 51

      Rate
      4.9

      Braking is noticeably weaker than Hankook, not so soft.
      Hankook bites dead into the asphalt when suddenly braking, these do not, but there is less wear and they catch better on snow.

      Vehicle:
      Renault Kangoo
      Control on a dry road
      Steering in the wet
      Drive comfort
      Course stability
      Quiet in motion
      Braking efficiency
      Resistant to aquaplaning
      Velocity characteristics
      Wearability
      Quality of production
      Price justifiability
      30 january 2016
    • Сергей Николаевич about tyre Michelin Agilis 51

      Rate
      4.3

      Very low and long mileage resistance results in economical tyres. Excellent ride comfort and handling. Intended for dry or wet roads, not winter conditions. The original factory tyres lasted around 100,000 km and now another set of the same tyres has been retreaded for around 40,000 km.

      Vehicle:
      Peugeot Partner
      Size:
      175/65 R14 T
      Buy again?:
      Definitely yes
      Control on a dry road
      Braking on dry roads
      Steering in the wet
      Braking on wet roads
      Drive comfort
      Internal noise
      External noise
      Wearability
      24 march 2011
    • Сергей about tyre Michelin Agilis 51

      Guys! I've driven on Agilis 41 for 120,000 km! They're still alive, but I'll need to replace them soon!

      16 february 2026
    • Владимир about tyre Michelin Agilis 51

      The product was purchased at Mosautoshina
      Rate
      5

      Very satisfied with the purchase

      Vehicle:
      Peugeot Traveller
      Size:
      215/65 R16C 106/104T
      Buy again?:
      Definitely yes
      City:
      Krasnodar
      Control on a dry road
      Steering in the wet
      Drive comfort
      Course stability
      Quiet in motion
      Braking efficiency
      Resistant to aquaplaning
      Velocity characteristics
      Wearability
      Quality of production
      Price justifiability
      13 july 2024
    • Дмитрий about tyre Michelin Agilis 51

      The product was purchased at Mosautoshina
      Rate
      4

      Unfortunately, the user did not write a comment on their review.

      Size:
      215/65 R16C 106/104T
      Rate
      20 april 2024
    View all 27 reviews about Michelin Agilis 51
    Tire specifications
    Dry road
    Wet road
    Comfort
    Course stability
    Silent
    Rate
    Braking
    Aquaplaning
    Speed
    Wearability
    Quality
    Price/performance
    4.01 / 5
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