top of page

Analysis Results JSON Field Descriptions

The analysis results file is obtained by downloading the JSON file from the link. The size of the file is dependent on the amount of data generated for the store visit and the sections that are provided to the customer. A good estimation for a modern trade store visit is roughly 1 MB. For a sample of the JSON file, see the example below. The Structure of the JSON is as follows:

session_uid string 

Session UUID generated by Trax 

client_session_uid string 

Session UUID generated by the client

 

client_type string 

Options: 

  • Trax Mobile

  • On-Device

  • API

  • Fixed Camera

project_name string 

Project name as provided by Trax

store_number string 

Client store unique identifier

external_route_id string 

Client route unique identifier

session_date string (YYYY-MM-DD) 

Visit date in local time zone

session_start_time number (Unix Epoch Time timestamp) 

The Unix Epoch Timestamp of the session start time as provided by the client application

session_end_time number (Unix Epoch Time timestamp) 

The Unix Epoch Timestamp of the session end time as provided by the client application. This value is empty in case: 

  • Session did not begin to upload

  • User did not click on the "End Visit" button

local_tz_offset  number

The local timezone offset from UTC in minutes (e.g. for PST will be -420), i.e., the offset between local time on the mobile device and UTC time

visit_uploading_end_time number (Unix Epoch Time timestamp) 

The Unix Epoch Timestamp of the last image in the session uploaded end time. This value will be empty if the last image in the session didn't finish uploading 

visitor_identifier string 

The user email 

visitor_service_type string 

Options: 

  • null

  • regular

  • live only 

  • live first

visit_type_uid string 

Visit UUID generated by trax

visit_type_name string 

Visit type name as provided by Trax

GPS_coordinates_latitude string 

Latitude coordinate of the visits as captured by the GPS. It is calculated as the median of all the GPS coordinates captured in the different scenes

GPS_coordinates_longitude string 

Longitude coordinate of the visits as captured by the GPS. It is calculated as the median of all the GPS coordinates captured in the different scenes

store_GPS_coordinates_latitude string 

Latitude coordinate of the visit store as entered in the DB

store_GPS_coordinates_longitude string 

Longitude coordinate of the store as entered in the DB

device_model string 

Device Model by which the visit was conducted

application_version string 

Application version of the mobile device used in the visit

session_closed_by_TTL number 

Options: 

  • 1 - Session is closed by Time To Leave service

  • 0 or null - Session is closed normally 

partial_GPS_coordinates boolean 

Options: 

  • true - Some probes do not have GPS coordinates identified

  • false - All probes have GPS coordinates identified 
     

cycles Object[] 

List of project-level visit cycles. Each cycle contains the following fields: 

cycle_name string 

Cycle name associated to the visit

cycle_start_date string (YYYY-MM-DD)

Cycle start date

cycle_end_date string (YYYY-MM-DD)

Cycle end date

results_metadata object

Additional properties. Contains the following fields: 

generation_time number (Unix Epoch Time timestamp)

The Unix Epoch Timestamp of analysis data generation

version number 

The report may have versions. For example, if an additional section has been

computed and added to an initial report, another version will be generated

status string

Options:

  • completed

  • partial

Partial reports are automatically generated by Trax when the expected SLA time has passed and some

data from the client has failed to arrive. For example, one or more images from a store visit failed to upload.

If and when the missing data will arrive, the results will be updated. 

sections_included String[]

The sections to be included in 'details' 

Note: The data returned is based on the agreement between Trax and the customer

blade_output_summary object

Trax's Big Data anomaly detector output, e.g.:

  • category_resolutions

    • successful: 1​

    • anomalies: 3

  • display_resolutions: 

    • successful: 0

    • anomalies: 1
       

details object

The analysis data. The possible fields are as follows. Note that the data is based on the

agreement between Trax and the customer:

images Object[]

A list of scenes objects with image related data, including stitched scene images,

the actual images and image quality issues. Each object will contain the following fields: 

scene_id number

Scene ID generated by Trax

scene_uid string

Scene UUID generated by Trax

scene_status string

Status of the scene that the image is associated with

scene_closed_by_TTL number

Options: 

  • 1 - The scene is closed by Time To Leave service

  • 0  or null - The scene is closed normally

client_scene_uid string

Scene ID provided by the client

store_area_code string

Store area code (that has been configured in coordination with Trax implementation team)

task_uuid string

Task UUID (that has been configured in coordination with Trax implementation team)

 

task_name string

Task name (that has been configured in coordination with Trax implementation team)

task_display_name string

Task display name (that has been configured in coordination with Trax implementation team)

task_code string

Task code (that has been configured in coordination with Trax implementation team)

thumbnail_url string

URL of a thumbnail image of the stitched scene

preview_url string

URL of a preview image of the stitched scene

scene_images Object[]

A list of the images comprising the scene. Each object will contain the following fields:​

image_uid string

Image UUID generated by the client during the visit

capture_time number (Unix Epoch Time timestamp)

Image capture time as provided by the client

quality_issues Object[]

Image quality related issues (if any). Should there be any issues, they will appear with the following information:

code number 

Error Code

value string 

Error value

The following are examples of Code - Value pairs. Note that the list of code-value pairs may vary per project

  • 1 - Bad capture angle

  • 2 - Nothing to tag

  • 3 - Too close

  • 4 -  Too far

  • 5 - Image rotated

  • 6 - Blurry

  • 8 - Reflection 

  • 9 - Suspected Fake

  • 13 - Obstacle

  • 45 - Other

  • 52 - Low Accuracy 

  • 64 - Faces visible in image

image_urls object

URLs of the image stored in different resolutions:

original string

URL of the original image sent by the client

medium string

URL of the an image compressed to medium size (about X3 smaller than original)

small string

URL of the an image compressed to small size (about X5 smaller than original)

categories Object[]

A list of categories covered in the visit. Each category will contain the following fields: 

id number

Internal Trax ID for the Category​

name string

Taken from a list of predefined categories as agreed between Trax and the client

anomalies Object[]

A list of anomalies detected by Blade, Trax Big Data anomaly detector. Each one will contain the following fields: 

code number

Anomaly code

value string

Anomaly value

The following are examples of Code - Value pairs. Note that the list of code-value pairs may vary per project

  • 40 - Low number of displays

  • 41 - Low number of bays

  • 42 - Missing required brands

  • 43 - Wrong template

  • 48 - Partial category 

  • 49 - Photo quality issues

  • 50 - Low accuracy 

  • 56 - Low number of facings
     

recognized_items/live_recognized_items Object[]

A list of regular/live recognition scenes with analyzed data per item. Each item will contain the following fields:

scene_id number

Scene ID generated by Trax
 

scene_uid string

Scene UUID generated by Trax

client_scene_uid string

Scene ID provided by the client

store_area_code string

Store area code (that has been configured in coordination with Trax implementation team)

task_uuid string

Task UUID (that has been configured in coordination with Trax implementation team)

task_name string

Task name (that has been configured in coordination with Trax implementation team)

task_display_name string

Task display name (that has been configured in coordination with Trax implementation team)

task_code string

Task code (that has been configured in coordination with Trax implementation team)

items/liveItems Object[]

A list of items with item details and aggregated data, regular/live recognition. Each item will contain the following fields:

id number

Internal Trax item ID

name string

Trax item name

short_name string

Short item name

code string

EAN or UPC code of the item (Refers to client_code in the pricing section/Get products)

type string

Product types:

  • SKU

  • POS

  • Other

  • Empty

  • Irrelevant

  • Brand Obfuscated 

Note: The list and values may vary per project

customer_product_id string

Customer product ID

client_code string

Client product code (Refers to alt_code in the pricing section/Get products) 

item_code string

Client item code 

Refers to the code in the pricing section and item_Code in Get products

product_uuid string

Trax product UUID

task_display_name string

Task display name (that has been configured in coordination with the Trax implementation team) 

count object

The aggregated facing count data per the item within the scene. The object contains the following fields:

total number

The total facings count of the item within the scene​

front number

The total front facings count of the item within the scene. For regular recognition only

side number

The total side facings count of the item within the scene. For regular recognition only

back number

The total back facings count of the item within the scene. For regular recognition only

top number

The total top facings count of the item within the scene. For regular recognition only

bottom number

The total bottom facings count of the item within the scene. For regular recognition only

calculations Object[] - NOTE THIS SECTION IS DEPRICATED!

A list of calculations as coordinated with the customer. These calculations are KPIs based on KPI Set (score-card) calculations.

Each calculation will contain the following fields:

name string

KPI set name

kps_score number

KPI total score

kps_details Object[]

List of KPI detailed results, each result will contained the following fields:

name string

KPI detail name

result string

KPI detail result

target string

KPI detail target

score number or "" (empty string)

KPI detail score

kpis/live_kpis Object[] 

A list of KPI/Live KPI and KPI feedback results. Each KPI object will contain the following fields:

scene_id number

Trax scene ID for scene level KPI

client_scene_id string

Client scene ID for scene level KPI

scene_uid string

Trax Scene UUID for scene level KPI

name string

KPI name

numerator number (whole)

Numerator result for KPI result represented as ratio, or any auxiliary result

denominator number (whole)

Denominator result for KPI result represented as ratio, or any auxiliary result

result string

KPI result, or any auxiliary result

target string

KPI target, or any auxiliary result

score string

KPI score, or any auxiliary result

weight string

KPI weight, or any auxiliary result

entities Object[]

List of entities for which the KPI was calculated. Each entity will contain the following fields:

type string

Entity type

uid number or string

Entity UUID, as defined for project

flexible_target Object[]

Flexible target date range, it will contain the following fields:

target_start_date string

Target start date​

target_end_date string

Target end date

feedback Object[]

KPI feedback made by a user. The structure will contain the following fields:

feedback string

Standard KPI feedback message

feedback_local string

Local KPI feedback message (customized)

feedback_time_local number

The Unix Epoch Timestamp when the feedback was made, in the local timezone

delete_time_local number

The Unix Epoch Timestamp when the feedback was deleted, in the local timezone

results  Object[]

Nested KPI results. The object structure is the same as the nesting one (all the above)

actions Object[] 

A list of actions with relevant data performed by the user. Each action object will contain the following fields:

name string

The action type. Currently, only out of stock is supported

Type string

Regular or live visit action 

Options:

  • Full Recognition

  • Live

Generator KPI string

KPI type used to generate the instances of the actions. For example, "OOS-SKU"

Actions Instances Object[]

List of actions instances presented to the user for a certain type. For example, for OOS type a list of instances will be the list of missing

SKUs that need to be replenished. Each action will contain the following fields: 

action_id number

KPI type used to generate the instances of the actions. For example, "OOS-SKU"

entity_type string

Entity type the action refers to. Currently, only product is supported

entity_name string

Entity name the action refers to. Currently, we only support Product, so it will be the name of the product​

entity_uid string

Entity UUID, e.g. UUID of missing product

entity_code string

Entity code the action refers to. Currently, we support only Product, so it will be the EAN of the product 

reason_type string

The type of the presented reason

reason_display_name string

Client name of the presented reason 

reason_standard_name string

Client standardized name of the presented reason   

status string

The status of the action. Two types of statuses are supported: Reviewed (the user handled the certain instance by providing the reason)

and Pending (user did not act and provide a reason for the instance). When the status is Pending, the reason type and reason display name are Null.

Generator KPI string

KPI type used to generate the instances of the actions. For example:  "OOS - SKU" ​

scene_uid string

Trax Scene UUID the action was captured for

validation_actions Object

The validation actions provided by the user. The fields of the object are:

validation_method String[]

The list of validation methods set for the action such as "validation-product-photo-close-up", "validation-product-barcode", etc. - defined per project

image_capture object

The image details sent by the user as part of the validation method. The object contains the following fields:

image_url string

The URL of the original image sent by the user as part of the validation method 

image_capture_time string (YYYY-MM-DD HH:MM:SS)

Image capture time as provided by the user

barcode_capture object

The barcode details provided by the user as part of the validation method. The object contains the following fields:

barcode string

The barcode provided by the user​

barcode_validation boolean

true = The barcode matches the barcode in the master data

live_actions Object[] 

A list of live actions bound to a particular KPI with related data including the action type and the reason selected by the user.

Each action object will contain the following fields:

name string

The action type. currently out out of stock is supported

Type string

Regular or Live visit action

Options:

  • Full Recognition 

  • Live
     

generator_kpi string

KPI type used to generate the instances of the actions. For example, "OOS-SKU"

actions_instances Object[]

List of actions instances presented to the user for a certain type. For example, for OOS type a list of instances will be the list of missing

SKUs that need to be replenished. Each action will contain the following fields: 

action_id string

ID of the action instance. For example, "Fill a certain SKU is considered an instance of the OOS type". Each missing SKU is an instance

entity_type string

Entity type the action refers to. Currently, only Product is supported​

entity_name string

Entity name the action refers to. Currently, only Product is supported​, so it will be the name of the product

entity_uid string

Entity UUID, e.g. UUID of missing product

entity_code string

Entity code the action refers to. Currently, only Product is supported​, so it will be the EAN of the product

reason_type string

The type of the presented reason

reason_display_name string

Client name of the presented reason

reason_standard_name string

Client standardized name of the presented reason 

status string

The status of the action. Two types of statuses are supported: Reviewed (the user handled the certain instance by providing the reason) 

and Pending (the user did not act and provide a reason for the instance). When the status is Pending, the reason type and reason display name are Null

scene_uid string

Scene UUID the action was captured for

validation_actions object

The validation actions done by the user. The fields of the object are as follows:

validation_method String[]

The list of validation methods set for the action such as "validation-product-photo-close-up", "validation-product-barcode", etc. - defined per project

image_capture object

The image details sent by the user as part of the validation method. The object contains the following fields:
 

image_url string

The URL of the original image sent by the user as part of the validation method 

image_capture_time string (YYYY-MM-DD HH:MM:SS)

Image capture time as provided by the user

barcode_capture object

The barcode details provided by the user as part of the validation method. The object contains the following fields:
 

barcode string

The barcode provided by the user​

barcode_validation boolean

true = The barcode matches the barcode in the master data

questionnaires Object[] 

Visit's questionnaires data. Each object will contain the following fields:

id number 

Internal Trax ID for the questionnaire

linked_entity object

The questionnaire's related entity. It will contain the following fields:​

entity string 

Linked entity name: 

  • SESSION

  • SCENE

entity_uid string 

The UUID of the entity​

questionnaire Object[] 

List of questions and answers. Each questionnaire object will ​contain the following fields:

code string 

Question code

question string 

Question text

survey_group_name string 

The group name of several questions. The questions appear in the mobile app in the same group

question_order number 

Question display order in the mobile app

question_type string 

Question type:

  • Radio

  • Checkbox

  • Numeric

  • Text

  • Radio + Free Text

  • Checkbox + Numeric

  • Numeric + Numeric

question_additional_attributes Object 

A list of question attributes. These attributes are coordinated with Trax implementation.

<key> String 

Attribute name as coordinated with Trax implementation.

<value> String 

Attribute name as coordinated with Trax implementation.

answer string 

Attribute name as coordinated with Trax implementation.

answer_additional_attributes Object 

A list of answer attributes. These attributes are coordinated with Trax implementation.

<key> String 

Attribute name as coordinated with Trax implementation.

<value> String 

Attribute name as coordinated with Trax implementation.

images_path string

Answer images paths separated by comma

pricing Object[] 

Items pricing data. Each object will contain the following fields:

id number 

Internal Trax ID for the questionnaire

name string 

Trax item name

local_name string 

Client local item name

code string 

EAN or UPC code

source string 

This field details the source of the pricing information:

  • recognition

  • manual
     

price_tag string 

The indication whether the product has a price tag or not:

  • Y - There is a price tag for the product

  • N - there is no price tag available for the product

client_code string 

Client product code​

alt_code string 

Client product alt code

product_uuid string 

Trax product UUID

promotion boolean 

Is the product in promotion? May be omitted if false

price object 

Price details. The object will contain the following fields:

median number 

Median - in case of an even sample set we take the minimal value of the two 

std number 

Standard deviation

 

 

 

 

 

 

manual_collection Object[] 

Manual Collection results. A manual collection is a "Product Attributes Survey" task that appear in the mobile app.

With this task, a user can manually collect data on specific products. Each object will contain the following fields:

products_metrics Object[] 

A list of products metrics. Each object will contain the following fields:

id number

Internal Trax item ID

name string

Trax product name

code string

EAN or UPC code

client_code string

Client product code

alt_code string

Client product alt code

product_uuid string

Trax product UUID

metrics object

Object of arrays of all attributes available in product attribute survey task, such as: 

barcodes Object[]

Represents barcodes scanned/collected for specific products. Each object will contain the following fields: 

name string

The name defined in the product attribute survey configuration task ​

value string

The actual collected value

prices Object[]

Represents prices collected for specific products. Users can have multiple prices such as: General Price, Member Price.

Each object will contain the following fields: 

name string

The name defined in the product attribute survey configuration task ​

value string

The actual collected value

promotion number

Is promotional price? 0/1 or omitted if not defined​

state string

Price collection state. May be omitted if not defined

numbers Object[]

Represents any numeric attributes collected for specific products. User can have multiple numbers such as: Inventory Amount, Boxes Amount.

Each object will contain the following fields: 

name string

The name defined in the product attribute survey configuration task ​

value string

The actual collected value

dates Object[]

Represents any date attributes collected for specific products. User can have multiple dates such as: Expiration Date, Date of Manufacture.

Each object will contain the following fields 

name string

The name defined in the product attribute survey configuration task ​

value string

The actual collected value

images and barcodes Object[]

Represents images collected for specific products. Each object will contain the following fields: 

name string

The name defined in the product attribute survey configuration task ​

image_url string

Image URL of the original image sent by the client as part of the validation method

name string

The name defined in the product attribute survey configuration task ​

image_capture_time number (Unix Epoch Time timestamp)

Image capture time as provided by the client 

barcode string

The actual collected barcode value ​

validation_status boolean

true = The barcode matches the barcode in the master data

collection_quality Object[] 

Visit collection quality section. Displays the visit, category, and display entities' quality and completeness flaws, success results and exclude/include results.

Each object will contain the following fields:

visit_resolutions object

Visit resolution section. Displays the quality and the completeness flaws in the visit level as well as success results and exclude/include results. It contains the following fields: 

trax_visit_excluded String

"true"/"false" for the visit is excluded from/included into the calculation

trax_visit_resolution String

"successful"/"unsuccessful" depending on the resolution ​

scenes Object[]

Scene and images laws per scene. Each object will contain the following fields: ​

scene_uid string

Scene UUID generated by Trax​

scene_anomalies String[]

List of scene level anomalies. Possible values include: (Note: the list and values may vary per project)

  • Wrong Scene Type

  • Photo Quality issues

  • Photos taken from different distances

  • Incomplete scene

  • Multiple scene types

  • No overlap between images

  • Missing required brands

  • Survey is missing

  • Stitching Issue

  • Suspected Fake

  • Technical Issue

  • Wrong Recognition

  • Scene Too Far

  • Obstacle 

  • Low Number of displays

  • Low Number of bays

  • Low validation accuracy 

  • Suspected Fake - Geo
     

scene_images_anomalies Object[]

Array of scene images anomalies. Each objection will contain the following fields: ​

visit_anomalies Object[]

Visit level flaws (Array of key/value pairs). Each object will contain the key "reason_text" and will contain one of the following values. Please note, the list and values may vary per project:

  • Scene is split to several scenes

  • Survey: photo not provided

  • Survey: Answer not confirmed

  • Low Number of displays

  • Survey is missing

  • Incomplete visit - Store closed

  • Incomplete visit - No access to stockroom

  • Incomplete visit - No permission to photograph

  • Incomplete Visit

  • Partial data received

  • Low number of bays

  • Scene Issues

  • Contains Incomplete Scenes

  • Photo Quality issues

  • Low Accuracy 

  • Contains Suspected Fake Scenes - Geo 

  • Unsuccessful By User ​

category_resolutions Object[] 

Category resolution section. Displays the quality and completeness flaws in the category level (each category) as well as success results and exclude/include results. Each object will contain the following fields: 

scene_id number

Scene ID generated by Trax

id number

Internal Trax Category ID​

name string

Category name​

trax_category_excluded string

"true"/"false" for the category is excluded from/included into the calculation 

trax_category_resolution string

"successful"/"unsuccessful" depending on the resolution 

 

anomalies Object[]

Array of anomalies detected by Blade, Trax Bid Data anomaly. Each object will contain the following fields:

code number

Anomaly code number

value string

Anomaly code value

The following are examples of Code - Value pairs. Note that the list of code-value pairs may vary per project

  • 40 - Low number of displays

  • 41 - Low number of bays

  • 42 - Missing required brands

  • 43 - Wrong template

  • 48 - Partial category 

  • 49 - Photo quality issues

  • 50 - Low accuracy 

  • 56 - Low number of facings


display_resolutions Object[] 

Display resolution section. Displays the quality and completeness flaws in the display level as well as success results and exclude/include results. Each object will contain the following fields:

trax_display_excluded string

"true"/"false" for the display is excluded from/included into the calculation​

trax_display_resolution string

"successful"/"unsuccessful" depending on the resolution

anomalies string

Display possible flaws. ​Below are some possible values. Note the list and values may vary per project:

  • Low Number of displays

  • Photo Quality issues

  • Low Accuracy

  • Partial/Missing Display Scenes

collection_discrepancies object

Discrepancies issues found in the collection process. The object will contain the following fields: 

possible_fake_gps_used boolean

true = Possible device GPS coordinates tampering​

distance_between_store_visit_location integer

The calculated straight distance between the visit GPS and the Store GPS coordinates, in m​

suspected_fraud object

Represents the visit level use cases suspected as fraud. The object will contain he following fields:

location_mismatch object

Information regarding the location of user in comparison to the store. The object will contain the following fields:

location_mismatch_store_master_data object

Detected suspected fraud by comparing the store and the visit GPS coordinates against the set threshold. The object will contain the following fields:

GPS_coordinates_latitude string

Latitude coordinate of the visits as captured by the GPS. It is calculated as the median of all the GPS coordinates captured in the different scenes

GPS_coordinates_longitude string

Longitude coordinate of the visits as captured by the GPS. It is calculated as the median of all the GPS coordinates captured in the different scenes

store_GPS_coordinates_latitude string

Latitude coordinate of the visit stored as entered in the DB

store_GPS_coordinates_longitude string

Longitude coordinate of the visit stored as entered in the DB

distance_between_store_visit_location float

Calculated straight distance between the visit and the store GPS coordinates, in km

distance_threshold float

Distance threshold, in km, defined per project​

GPS_suspected_fraud_store_based boolean

true = If the distance is above the threshold​

no_result_reason string[]

Possible values:​

  • No coordinates master data for this store

  • No GPS coordinates data for this visit
     

location_mismatch_previous_visits object

Detected suspected fraud by comparing the media of the last two visits and the current visit GPS coordinates against the set threshold. The object will contain the following fields: 

GPS_coordinates_latitude string

Latitude coordinate of the visits as captured by the GPS. It is calculated as the median of all the GPS coordinates captured in the different scenes

GPS_coordinates_longitude string

Longitude coordinate of the visits as captured by the GPS. It is calculated as the median of all the GPS coordinates captured in the different scenes

previous_visits_GPS_coordinate_latitude string

Median calculation of the last two visits GPS latitude coordinate in the different scenes (all probes) need to have at least 50% of probes with GPS location if not this will be null​

previous_visits_GPS_coordinate_longitude string

Median calculation of the last two visits GPS longitude coordinate in the different scenes (all probes) need to have at least 50% of probes with GPS location if not this will be null​

distance_between_previous_current_visit_location number

Calculated straight distance between current visit and last two visits GPS coordinates, in km​

distance_threshold number

Distance threshold, in km, defined per project

GPS_suspected_fraud_previous_visits boolean

true = If the distance is above the threshold

No_result_reason String[]

Possible values​:

  • No two validated previous visits for this store

  • No GPS coordinates data for this visit

  • At least one of the previous visits suspected as fraud

  • At least one of the previous visits has no GPS coordinates
     

images_suspected_as_fake object

Information regarding the images that were marked as suspected as fake. The object will contain the following fields:

image_uid string

Image UID generated by Trax

image_suspected_as_fake_manual boolean

true = Marked as suspected as fake in a manual process​

Shelf_position 

A list of scenes with shelf position data per item 

Risks of using this data:

  • With raw data customers can calculate many KPIs, however, without the adjacency graphs that we have in Trax to calculate position KPIs (such as blocking and adjacencies), customers will not be able to get to the level of accuracy that we do.

  • The sequence and stacking layer data are influenced by the image collection quality and angle – it could be wrong.

  • Business logic rules do not apply to this data.

scene_uid  string

Scene UUID generated by Trax 

client_scene_uid  string

Scene ID provided by the client 

total_number_of_bays  number

Total number of bays in the scene  

total_number_of_shelves  number

Total number of shelves in the scene  

items  object[]

A list of items with item details and position data on a specific shelf and bay

 

bay_number  number

The bay number that this item was recognized and positioned 

 

shelf_number  number

The shelf number that this item was recognized and positioned   

product_name  string

Trax item name  

ean_code  string

EAN or UPC code if the item (refers to client_code in the pricing section/get products)  

product_type  string

Product types: SKU, POS, Other, Empty, Irrelevant, Brand Obfuscated​

Note: the list and values may vary per project

category_name  string

Unique category name related to the product 

 

category_local_name  string

Unique category name related to the product in the project's local language 

sub_category_name  string

Unique subcategory name related to the product 

sub_category_local_name  string

Unique subcategory name related to the product in the project's local language 

brand_name  string

Unique brand name related to the product 

brand_local_name  string

Unique brand name related to the product in the project's local language  

position  object[]

The sequence number and stacking layer per item on the shelf

 

facing_sequence_number  number

Facing the sequence number of this item on the shelf 

Stacking_layer  number

Stacking Layer number: 1 – Close to the shelf, 2 – second layer of items, 3 – third layer of items, etc. 

{

"session_uid": "e9599eb1-f352-4b90-a287-a5ac1e1befb2",

"client_session_uid": "e9599eb1-f352-4b90-a287-a5ac1e1befb3",

"client_type": "On-Device",

"project_name": "projectName",

"store_number": "100",

"external_route_id": "2",

"session_date": "2017-03-22",

"session_start_time": 1490183405,

"session_end_time": 1490183406,

"visit_uploading_end_time": 1490183407,

"visitor_identifier": "sally@example.com",

"visit_service_type": "regular",

"visit_type_uid": "c0bd9d1c-cec5-4021-969b-ecbd34d585ef",

"visit_type_name": "Standard visit",

"GPS_coordinates_latitude": 61.5896551,

"GPS_coordinates_longitude": 14.1524749,

"store_GPS_coordinates_latitude": 61.5346551,

"store_GPS_coordinates_longitude": 14.2234749,

"device_model": "samsung8",

"application_version": "mobile_android-1.29.11.0",

"session_closed_by_TTL": 0,

"partial_GPS_coordinates": false,

"cycles": [

{

"cycle_name": "cycle 1",

"cycle_start_date": "2019-01-02",

"cycle_end_date": "2019-02-02"

}

"results_metadata": {

"generation_time": 1490184185,

"version": 1,

"status": "completed",

"sections_included": [

"calculations",

"recognized_items",

"live_recognized_items",

"images",

"categories",

"pricing",

"questionnaires",

"manual_collection",

"actions",

"live_actions",

"kpis",

"live_kpis",

"collection_quality"

],

"blade_output_summary": {

"category_resolutions": {

"successful": 1,

"anomalies": 3

},

"display_resolutions": {

"successful": 0,

"anomalies": 0

}

}

},

"details": {

"images": [

{

"scene_id": 12054,

"scene_uid": "e9599eb1-f352-4b90-a287-a5ac1e1befb7",

"scene_status": "Completed",

"scene_closed_by_TTL": 0,

"client_scene_uid": "9586335",

"store_area_code": "regular_checkouts_area",

"task_uuid": "392afe28-5119-462d-b7c6-40c28c2a5d21",

"task_name": "Display",

"task_display_name": "display_name",

"task_code": 159,

"thumbnail_url": "https://services.traxretail.com/crypt/traxus/

ecu3GZGk/myB8hDvrzpg/aQBrqzsLeFThbpsXiStP

E5LcUtAKl47eIkwF+U/8R8vdylVKsOIgZPW33UTwa9T",

"preview_url": "https://services.traxretail.com/crypt/traxus/

hWK8AWOedKDq0wUt7/mDAVWPPYJK1l4iBQf9bmGF9mOqgQ

shqMr4P6TJ",

"scene_images": [

{

"capture_time": 1490183407,

"quality_issues": [ { "code": 123, "value": "Too far" } ],

"image_urls": {

"original": "https://services.traxretail.com/crypt/traxus

/Dlh9a0Ge5sVLSWVJUvMLeQIn9eKR6S97

WxHaI5wdFj0B6fnh6pspUanyNrut/bLsc84uZACnfyz",

"medium": "https://services.traxretail.com/crypt/traxus/

KhWxSufAkDnMiH8U8TcEXZi7Uu59lRNuNlVdhlyY

+WEEz8X/bMOO3LdzQ7pkL0ggkuKQ",

"small": "https://services.traxretail.com/crypt/traxus/

p7PAwvofqX2lSkTxCZ1fU+S87rYrUUX2t9+

HV4muQcRGCx1kkmWvz9QB6P9heVwjZlNR/FOIYMLizM"

}

},

{

"capture_time": 1490183410,

"quality_issues": [ { "code": 121, "value": "Too blurry" } ],

"image_urls": {

"original": "https://services.traxretail.com/crypt/

traxus/sTrSB2Y9ozlbEAKbCGdVlZLTtkX

DyC6uj+VG9WyhWUTS6TZvHls2DIFKYaVa

ejx1nJ9hM5Q33YwJiiR",

"medium": "https://services.traxretail.com/

crypt/traxus/CgrKFtI6eZ9+rUT3IM

Y06yoXI+O8L3RPlcBK6WjtxS0AyhGi1

e0dCbPn9IU7d37htzCgu1Q8YNFk",

"small": "https://services.traxretail.com/

crypt/traxus/kRUJXzMFltUf2obnUdVN8

euwnXl+D1zxYKWqw0GKWEZVp5ww+sO+oFt

mCANQ2UrAzxlD8WNZ/oMrlB8mSl"

}

}

]

},

{

"scene_id": 12055,

"scene_uid": "e9599eb1-f352-4b90-a287-a5ac1e1befb8",

"scene_status": "Completed",

"scene_closed_by_TTL": 1,

"client_scene_uid": "amuy8523",

"store_area_code": "main_aisle_area",

"task_uuid": "392afe28-5119-462d-b7c6-40c28c2a5d31",

"task_name": "Display",

"task_display_name": "display_name",

"task_code": 888,

"thumbnail_url": "https://services.traxretail.com/crypt/traxus/

2FTed2SEOxTV/2aBMUWJJA/

FacdnWdUgSbLqKRBZbr4cMEoq3KFOfv3qWhm1DoRlL/

ZG0O52y5iRVXH36",

"preview_url": "https://services.traxretail.com/crypt/traxus/ABJrbfQITrXy

JjgdWiFb8gB+5NUzbxHcTV2qxX7/Pb6jAfB3BqYq8HT5KTMYSPN6J=",

"scene_images": [

{

"capture_time": 1490183413,

"image_urls": {

"original": "https://services.traxretail.com/crypt/traxus/

2M31FyFOS85+Kuf5azfgdzmvugVUANfQoacf88AaGfKY

0lEsNpLK/p+bRta9EMPq47177HWp4B/

iSAo3fEtHdrpnP3o=",

"medium":"https://services.traxretail.com/crypt/traxus/

rlKkj3KgUl9r7GQy8nqYv84bjDmvq7m2Yb0Kq0a4Cq

tUOk94g6FnfRc++Hn/psLL8kpvNYjZSx1IDIt/pVyzY

YBdXD9/iJqqOY88ylr2gAmtiRQ=="

"small": "https://services.traxretail.com/crypt/

traxus/kRUJXzMFltUf2obnUKJGDKJeuwnXl+

D1zxYKWqw0GKWEZVp5ww+sO+oFtm

CANQ2UrAzxlD8WNZ/oMrlB8mSl"

}

}

]

} ],

"categories": [

{

"id": 3,

"name": "Coffee",

"anomalies": [ ]

},

{

"id": 5,

"name": "Pet food",

"anomalies": [

{

"code": 42,

"value": "Missing required brands"

},

{

"code": 48,

"value": "Partial Category"

},

{

"code": 56,

"value": "Low Number of facings"

}

]

}

],

"recognized_items": [

{

"scene_uid": "e9599eb1-f352-4b90-a287-a5ac1e1befb7",

"scene_id": 388927,

"client_scene_uid": "9586335",

"store_area_code": "frozen_area",

"task_name": "Display",

"task_display_name": "display_name",

"task_code": 159,

"task_uuid": "c24524bd-6675-11e9-9629-42010a5701c7",

"items": [

{

"id": 500,

"name": "Fanta 330 can",

"short_name": "Fanta 330 can",

"task_display_name": "display_name",

"code": "1234567891123",

"client_code": "2233",

"type": "SKU",

"item_code": "5566",

"product_uuid": "18f0fokn-c00a-31e5-b4ba-02c9c691",

"customer_product_id": null,

"count": {

"total": 20,

"front": 5,

"side": 5,

"back": 5,

"top": 5

}

},

{

"id": 0,

"name": "General Empty",

"short_name": "General Empty",

"task_display_name": "display_name",

"code": null,

"client_code": "",

"type": "Empty",

"item_code": null,

"customer_product_id": null,

"product_uuid": "00000000-0000-0000-0000-000000000000",

"count": { "total": 3 }

}

]

}

],

"live_recognized_items": [

{

"scene_uid": "e9599eb1-f352-4b90-a287-a5ac1e1befb7",

"scene_id": 388927,

"client_scene_uid": "9586335",

"store_area_code": "frozen_area",

"task_name": "Display",

"task_display_name": "display_name",

"task_code": 159,

"task_uuid": "c24524bd-6675-11e9-9629-42010a5701c7",

"liveItems": [

{

"id": 500,

"name": "Fanta 330 can",

"short_name": "Fanta 330 can",

"task_display_name": "display_name",

"code": "1234567891123",

"client_code": "2233",

"type": "SKU",

"item_code": "5566",

"product_uuid": "18f0fokn-c00a-31e5-b4ba-02c9c691",

"customer_product_id": null,

"count": { "total": 20 }

},

{

"id": 0,

"name": "General Empty",

"short_name": "General Empty",

"task_display_name": "display_name",

"code": null,

"client_code": "",

"type": "Empty",

"item_code": null,

"customer_product_id": null,

"product_uuid": "00000000-0000-0000-0000-000000000000",

"count": { "total": 3 }

}

]

}

],

"pricing": [

{

"id": 500,

"name": "Fanta 1L",

"local_name": "Fanta 1L",

"source": "recognition",

"price_tag": "N",

"code": "1234567891123",

"client_code": "1234567891123",

"alt_code": "5566",

"product_uuid": "25kjd-nyh-85pkjd-369-rft34",

"promotion": true,

"price": {

"median": 1,

"std": 0.1

}

},

{

"id": 80,

"name": "Fanta 330 can",

"local_name": "Fanta 330 can",

"source": "manual",

"price_tag": "Y",

"client_code": "4582164",

"product_uuid": "58fg-3954-85pkjd-369-rft34",

"price": {

"median": 896,

"std": 0

}

}

],

"calculations": [

{

"name": "RED SCORE gasoline station medium",

"kps_score": 90.5,

"kpi_details": [

{

"name": "Display 1",

"result": "83.5",

"target": "100",

"score": 83.5

},

{

"name": "Products comprise rectangular block",

"result": "true",

"target": "", "score": ""

},

{

"name": "List all the products on eye level",

"result": "Fanta 3243; Coke 434;

Zero 200" "target": "",

"score": ""

}

]

}

],

"questionnaires": [

{

"id": 23,

"linked_entity": {

"entity": "SCENE",

"entity_uid": "e9599eb1-f352-4b90-a287-a5ac1e1befb7"

},

"questionnaire": [

{

"code": "SF43",

"question": "How did you like the coffee?",

"survey_group_name": "On Trade survey",

"question_order": 1,

"question_type": "Radio",

"answer": "Wasn't good"

}

]

},

{

"id": 26,

"linked_entity": {

"entity": "SESSION",

"entity_uid": "e9599eb1-f352-4b90-a287-a5ac1e1befb7"

},

"questionnaire": [

{

"code": "FS46",

"question": "How did you like the coffee?",

"survey_group_name": "On Trade survey",

"question_order": 1,

"question_type": "Radio",

"answer": "Wasn't good"

}

]

}

],

"kpis": [

{

"scene_id": "1234",

"scene_uid": "ABCD",

"client_scene_uid": null,

"name": "SOS by facings",

"entities": [

{

"type": "MANUFACTURER",

"uid": "MANUFACTURER UID"

},

{

"type": "CATEGORY",

"uid": "CATEGORY UID"

}

],

"numerator": 50,

"denominator": 200,

"result": "0.25",

"score": "100",

"target": "0.20",

"flexible_target": [

{

"target_start_date": "2019-09-01T00:00:00.000Z",

"target_end_date": "2019-09-30T00:00:00.000Z"

}

],

"weight": "1"

},

{

"scene_id": "1234",

"scene_uid": "ABCD",

"client_scene_uid": null,

"name": "SOS by facings",

"entities": [

{

"type": "MANUFACTURER",

"uid": "MANUFACTURER UID"

},

{

"type": "CATEGORY",

"uid": "CATEGORY UID"

}

],

"numerator": 100,

"denominator": 200,

"result": "0.5",

"weight": "1"

},

{

"scene_id": "1234",

"scene_uid": "ABCD",

"client_scene_uid": null,

"name": "OOS",

"entities": [

{

"type": "PRODUCT",

"uid": "PRODUCT UID"

}

],

"result": "1",

},

{

"scene_id": "1234",

"scene_uid": "ABCD",

"client_scene_uid": null,

"name": "SOS by facings part of score",

"entities": [

{

"type": "MANUFACTURER",

"uid": "MANUFACTURER UID"

},

{ "

type": "CATEGORY",

"uid": "CATEGORY UID"

}

],

"numerator": 50,

"denominator": 200,

"result": "0.25",

"score": "100",

"target": "0.20",

"flexible_target": [

{

"target_start_date": null,

"target_end_date": null

}

],

"weight": "1",

"results": [

{

"name": "SOS by facings part of score level 2",

"entities": [

{

"type": "MANUFACTURER",

"uid": "MANUFACTURER UID"

},

{

"type": "CATEGORY",

"uid": "CATEGORY UID"

}

],

"numerator": 50,

"denominator": 200,

"result": "0.25",

"score": "100",

"target": "0.20"

}

]

}

],

"live_kpis": [

{

"scene_id": null,

"scene_uid": null,

"name": "OOS_PRODUCT_IN_ALL_STORE_LIVE_SESSION",

"entities": [

{

"type": "product",

"uid": "Moya Semya - Apple - 1L"

},

{

"type": "store",

"uid": "3800120433 АО ДИКСИ ЮГ"

},

{

"uid": null

}

],

"numerator": 1,

"denominator": 1,

"result": "OOS",

"score": "0",

"target": null

},

{

"scene_id": null,

"scene_uid": null,

"name": "DST_MANUFACTURER_IN_ALL_STORE_LIVE_SESSION",

"entities": [

{

"type": "manufacturer",

"uid": "TCCC"

},

{

"type": "store",

"uid": "3800120433 АО ДИКСИ ЮГ"

},

{ "uid": null }

],

"numerator": 0,

"denominator": 1,

"result": "0.00",

"score": "0.00",

"target": null

}

],

"manual_collection": {

"products_metrics": [

{

"id": 48,

"name": "my product name",

"code": "my product code",

"client_code": "my product client_code",

"alt_code": "my product alt_code",

"product_uuid": "8546-nyh-85pkjd-369-rft34",

"metrics": {

"barcodes": [

{

"name": "Barcode",

"value": "5454542"

}

],

"prices": [

{

"name": "promo",

"value": "12.90",

"promotion": true,

"state": "some_state"

}

],

"numbers": [

{

"name": "Face Count",

"value": "5154"

}

],

"dates": [

{

"name": "Expiry Date",

"value": "20-08-2018"

 

}

],

"images and barcodes": [

{

"name": "name",

"image_url": "https://services.traxretail.com/

crypt/traxusint/DELl/

aNFBxSp8KZmKhAZnXphO

50pHFgjnYS0LqIkZlvl5W5FwXMV6Hk",

"image_capture_time": 1613952000,

"barcode": "12345678",

"validation_status": false

}

}

}

]

},

"actions": {

"name": "OOS",

"Generator KPI": "SF_GSK_MSLOOS_by_Product_by_Store",

"Type": "Full Recognition",

"Actions Instances": [

{

"action_id": 779588,

"scene_uid": null,

"entity_type": "Product",

"entity_uid": "747f8cb0-3745-11ec-92d9-0bdd2224b4a9",

"entity_name": "Corega Denture Cleaning Tablets Bleaching Freshness Of

Triple Mint Cardboard Box 30 Tablets",

"entity_code": "4047400115350",

"reason_display_name": "OOS-Correct Tag",

"reason_type": "Product is not recognized",

"status": "Reviewed",

"Generator KPI": "SF_GSK_MSLOOS_by_Product_by_Store",

"validation_actions": {

"validation_method": [ "validation-product-photo-close-up", "validation-product-barcode" ],

"image_capture": {

"image_url": "https://traxus.s3.us-east-1.amazonaws.com/mdlzrusf/Images/products/806/

5addd68d-0aaa-4ae3-ac23-d766f51b6423/original",

 

"image_capture_time": "2022-05-24 09:31:09"

},

"barcode_capture": {

"barcode": "12345678",

"barcode_validation": true

}

}

},

{

"action_id": 779590,

"scene_uid": null,

"entity_type": "Product",

"entity_uid": "7499a460-3745-11ec-92d9-0bdd2224b4a9",

"entity_name": "Solpadeine Fast Medicines Painkillers Instrument Cardboard Box 12 Tablets",

"entity_code": "4602233005669",

"reason_display_name": "OOS-Correct Tag",

"reason_type": "Product is not recognized",

"status": "Reviewed",

"Generator KPI": "SF_GSK_MSLOOS_by_Product_by_Store",

"validation_actions": {}

}

]

},

"live_actions": {

"name": "Live OOS - SKU",

"generator_kpi": "Live OOS - SKU",

"Type": "Live",

"actions_instances": [

{

"action_id": 7955587,

"entity_uid": "9ca5a740-85cb-11ec-9ecc-4730ecacba76",

"reason_type": "Product is not recognized",

"reason_display_name": "Not OOS",

"status": "Reviewed",

"entity_type": "Product",

"entity_name": "Jubilee Traditional Biscuits 268 g",

"entity_code": "7622201639167",

"scene_uid": null,

"validation_actions": {}

},

{

"action_id": 7955594,

"entity_uid": "c21ae680-9312-11ec-9586-2f56ad64cdf9",

"reason_type": "Not available in store",

"reason_display_name": "OOS-Correct Tag",

"status": "Reviewed",

"entity_type": "Product",

"entity_name": "Barny-Milka Assortment Soft Cakes Gift Pack",

"entity_code": "7622201638887",

"scene_uid": null,

"validation_actions": {

"validation_method": ["validation-product-photo-close-up", "validation-product-barcode" ],

"image_capture": {

"image_url": "https://traxus.s3.us-

east1.amazonaws.com/mdlzrusf/Images/

products/806/5addd68d-0aaa-4ae3-ac23-

d766f51b6423/original",

"image_capture_time": "2021-11-29 15:20:21"

},

"barcode_capture": {

"barcode": "04092122",

"barcode_validation": false

}

}

}

]

},

"collection_quality": {

"visit_resolutions": {

"trax_visit_excluded": "false",

"trax_visit_resolution": "unsuccessful",

"scenes": [

{

"scene_uid": "0000-0000-0000-0001",

"scene_anomalies": [ "Incomplete scene", "Wrong Scene Type" ],

"scene_images_anomalies": [

{

"image_uid": "0000-0000-0000-1234",

"image_flaw": "Nothing to tag"

}

]

},

{

"scene_uid": "0000-0000-0000-0002",

"scene_anomalies": [ "Multiple scene types" ],

"scene_images_anomalies": [

{

"image_uid": "0000-0000-0000-7890",

"image_flaw": "Bad capture angle"

}

]

}

],

"visit_anomalies": [

{ "reason_text": "Survey: photo not provided" }

]

},

"category_resolutions": [

{

"scene_id": 1,

"id": 1,

"name": "SSD",

"anomalies": [

{

"code": 40,

"value": "Low Number of displays"

}

],

"trax_category_resolution": "unsuccessful",

"trax_category_excluded": "true"

}

],

"display_resolutions": [

{

"trax_display_excluded": "false",

"trax_display_resolution": "unsuccessful",

"anomalies": "Low Number of displays"

}

],

"collection_discrepancies": {

"possible_fake_gps_used" : false,

"distance_between_store_visit_location": 132

},

"suspected_fraud":

{

"location_mismatch":

{

"location_mismatch_store_master_data": {

"distance_between_store

_visit_location":6291.75257,

"GPS_suspected_fraud_store_based": true,

"store_GPS_coordinates_latitude": "52.612888",

"store_GPS_coordinates_longitude": "38.4814",

"distance_threshold": 1,

"GPS_coordinates_latitude": "7.0803549",

"GPS_coordinates_longitude": "79.9028834"

},

"location_mismatch_previous_visits": {

"distance_between_previous_current

_visit_location": null,

"GPS_suspected_fraud_previous_visits": null,

"no_result_reason": ["No two validated previous visits for this store"],

"distance_threshold": 1,

"GPS_coordinates_latitude": "7.0803549",

"GPS_coordinates_longitude": "79.9028834"

}

},

"images_suspected_as_fake": [

{

"image_uid": "8054788e-3a1c-4f0a-9c46-

d0435cd73289",

"image_suspected_as_fake_manual": true

}

]

}

}

}

}

}

Analysis Results JSON Example

bottom of page