DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
This action is in response to the applicant’s communication filed on 6/22/2026
Claims 1-2, 4-10, and 12-16 are pending. Claims 3 and 11 are canceled.
Response to Arguments
Applicant’s arguments with respect to claim(s) 1-16 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claim(s) 1, 4, 6-9, 12, and 14-16 is/are rejected under 35 U.S.C. 103 as being unpatentable over Barnes USPGPUB 2023/0300623 A1 (hereinafter Barnes) in view of Walsh et al. USPGPUB 2023/0319158 A1 (hereinafter Walsh), and further in view of Lem USPGPUB 2019/0303819 A1 (hereinafter Lem).
Regarding claim 1, Barnes teaches a method of industrial internet of things (IIOT) data conversion and distribution (Fig. 1, Par. [0007] “computer system translates, based at least in part on the identified electronic device, the data from the first format to a second format, where the second format is common to additional data associated with multiple different types of electronic devices, and where the additional data is stored in or is associated with the computer system.”; Par. [0061] “Access points 116, radio nodes 118 and/or services manager 130 may: manage network across different physical layers, provide sensor-to-backend management, and/or distributed decision-making.”; Par. [0047] “By translating the data to the second format, these communication techniques may address current obstacles and enable IoT applications and services”), the method comprising:
obtaining a first sensor input signal from an IIOT device (Par. [0007] “the computer
system obtains the data associated with the electronic device, which has a first format.”; Par. [0063] “type of the electronic device (such as a video camera, a temperature sensor, a humidity sensor, a microphone, etc.)”);
converting the first sensor input signal to a second sensor input signal having a common
data format based on one or more data conversion rules (Par. [0046] “computer system may translate, based at least in part on the identified electronic device, the data from the first format to a second format, where the second format is common to additional data associated with multiple different types of electronic devices” – basing the translation at least in part on the identified electronic device implies there are data conversion rules depending on the device type; Par. [0047] “This common format may enable data aggregation, analysis, decision-making and, thus, a wide variety of applications without requiring a standard, retrofitting of legacy equipment or the use of dedicated equipment”);
appending the second sensor input signal with a variable that describes information relating to the first sensor input signal, wherein the variable identifies a characteristic associated with the sensor data (Par. [0063] “computer system 132 may compute a context of the data. Moreover, the context may include: a location of electronic device 132 (which may be specified by or may correspond to GPS or local positioning system coordinates, a port, a network identifier, a VLAN identifier, etc.) a type of the data (such as environmental data, video, sound, etc.), a type of the electronic device (such as a video camera, a temperature sensor, a humidity sensor, a microphone, etc.), the first format, and/or a gateway (such as access point 116-1 or radio node 118-1 in a network) that forwards the data from electronic device 110-1 to computer system 132.” – one of ordinary skill in the art would understand that computing a context of the data would result in having additional useful data that would normally be appended to a data set for potential downstream use.);
Barnes does not explicitly teach broadcasting the second sensor input signal in the common data format to a particular data storage location, wherein:
the broadcasting the second sensor input signal includes communicating, using a public application programming interface (API) set, to the particular storage location included in a data storage according to the characteristic associated with the sensor data, and
the particular storage location is established for a receiving system to listen for the second sensor input signal from the HOT device;
responsive to detection by the receiving system of the second sensor input signal at the particular storage location, enabling access of the second sensor input signal by the receiving system;
sending an instruction to actuate a warehouse operation to the receiving systems based on the second sensor input signal accessed at the particular data storage location; and
controlling one or more operations of a warehouse based on the instruction to actuate the warehouse operation.
However, Walsh teaches broadcasting the second sensor input signal in the common data format to a particular data storage location (Fig. 14-15, Par. [0005], “receiving at a message collector module one or more messages formed by the platform gateway module; transmitting from the message collector module, the received one or more messages to a platform persistence module for storage”; Par. [0062], “The Persistence module 235 is the data store for each app. Each platform application, at provisioning / data modeling time, can decide what kind of persistent store they would like, such as an RDBMS, NoSql, newSQL, columnar, graphical, etc.”), wherein:
the broadcasting the second sensor input signal includes communicating, using a public application programming interface (API) set (Par. [0053], “Platform module 104 services can be accessed by Applications module 106 either by invoking the public interfaces using remote procedure call (RPC) or by calling web services of the platform”; Par. [0063], “Platform module 104 services can be accessed by Applications module 106 either by invoking the public interfaces using remote procedure call (RPC) or by calling web services of the platform. It is the external end point to be used by all to access the functionality within App SDK.”), to the particular storage location included in a data storage according to the characteristic associated with the sensor data (Par. [0042], “transforming the resource data to a Platform-specific format”; Par. [0062], “The Persistence module 235 is the data store for each app. Each platform application, at provisioning / data modeling time, can decide what kind of persistent store they would like, such as an RDBMS, NoSql, newSQL, columnar, graphical, etc.”; Par. [0007], “determining at the platform gateway module, a type of resource for the received data; and transforming at the platform gateway module, the received data into the message based on the determined type of resource, where the message is a format usable by the message collector module.”– resource type is interpreted as a characteristic associated with the sensor/resource data, and Walsh uses that characteristic to transform the data into a platform message.), and
the particular storage location is established for a receiving system to listen for the second sensor input signal from the IIOT device (Par. [0038], “The Persistence module 235 is the data store for each app. Each platform application, at provisioning / data modeling time, can decide what kind of persistent store they would like, such as an RDBMS, NoSql, newSQL, columnar, graphical, etc.”; Par. [0045], “Devices (more generally Resources), including Sensors (''S") and Actuators ("A") and humans or other organisms (not shown), are connected in a wired or wireless fashion (or via another Device) to Controllers. Sensors detect or measure some condition, for example temperature or the presence or absence of some chemical, and produce a signal”; Par. [0042], “transforming the resource data to a Platform-specific format”; Par. [0053], “Applications module 106 can subscribe to events produced by the Platform module 104 so that they receive those events as and when generated.”; Par. [0056], “The Message Listener module 221 listens for events from the Platform Core module 210, and then routes the message to the correct workflow” – The resource data after transformation into a platform specific message corresponds to the second sensor input signal, and the message listening module correspond to the receiving system listening for the second sensor input signal.); and
responsive to detection by the receiving system of the second sensor input signal at the particular storage location (Par. [0008], “receiving at a message listener module, the one or more events; and determining at the message listener module, a next step in a workflow based on the received event.”; Par. [0056], “The Message Listener module 221 listens for events from the Platform Core module 210, and then routes the message to the correct workflow”), enabling access of the second sensor input signal by the receiving system (Par. [0063], “The App Platform API module 237 is all of the workflows and persistence data stored … The way for an App (239, 241, 243) to interact with the Platform where the authentication, authorization, and the how is already baked into the App SDK. … The Apps (239, 241, 243) may access the Persistence module 235 to obtain needed information for a workflow.”).
Barnes and Walsh are analogous art because they are from the same field of endeavor and contain functional similarities. They both relate to internet of things data processing and distribution.
Therefore, at the time of effective filing date, it would have been obvious to a person of ordinary skill in the art to modify the above data conversion and distribution method, as taught by Barnes, and incorporate broadcasting using a public API, and workflow architecture to provide standardized access to converted IIOT data, app-specific storage, event subscription, message listening, and workflow routing, as taught by Walsh.
One of ordinary skill in the art would have been motivated to efficiency of IoT applications, enabling both a rapid time to market and regular improvements, as suggested by Walsh (Par. [0078]).
Barnes and Walsh do not explicitly teach sending an instruction to actuate a warehouse operation to the receiving systems based on the second sensor input signal accessed at the particular data storage location; and
controlling one or more operations of a warehouse based on the instruction to actuate the warehouse operation.
However, Lem teaches sending an instruction to actuate a warehouse operation to the receiving systems based on the second sensor input signal accessed at the particular data storage location (Fig. 1, Par. [0012] “system for managing tasks during the operation of a warehouse, the system comprising: (a) setting at least one initial parameter for the operation of the warehouse; (b) setting at least one priority during the operation of the warehouse; (c) commencing and monitoring the actual operation of the warehouse; (d) comparing the actual operation of the ware house compared with the at least one priority and the at least one initial parameter to determine whether the operation of the warehouse is considered on track or off track; and (e) taking remedial steps based on the comparison from step (d).”); and
controlling one or more operations of a warehouse based on the instruction to actuate the warehouse operation. (Par. [0047] “"task engine distributor" ("TED"), which is an intelligence engine dynamically reallocating work tasks based on past, present or future properties, such as worker profile (certification, skill level, equipment, current activity), their location in the facility, and changing priorities that happen over the course of an assigned task or day.”).
Barnes, Walsh, and Lem are analogous art because they are from the same field of endeavor. They relate to industrial internet of things.
Therefore, at the time of effective filing date, it would have been obvious to a person of ordinary skill in the art to modify the above data conversion, distribution method, and workflow system, as taught by Barnes and Walsh, and incorporate automated warehouse workflow control, as taught by Lem.
One of ordinary skill in the art would have been motivated to improve “automated workflows that can adjust based on situational awareness” as suggested by Lem (Par. [0009]).
Regarding claim 4, the combination of Barnes, Walsh, and Lem teaches all the limitations of the base claims as outlined above.
Lem further teaches wherein controlling the one or more operations of the warehouse
include:
monitoring behavior of one or more warehouse operators (Par. [0054] “the present invention tracks “priorities” as initially defined in the initial environment as well as the delta between tasks done and to be done, where people are and what tasks they are performing”),
specifying an item to be retrieved from the warehouse (Par. [0060] “a number of different pick options including Pick faces Only, FIFO or LIFO, Fast Pick, Stable Pallet, Minimize Honeycomb, Minimize Travel, Output Level. Warehouse Operational options may include Pallet Cube Size, Serpentine YIN, Labour Units, Standby Labor, Hrs per Shift and other production benchmarks.”),
specifying a quantity of the item to be retrieved from the warehouse (Par. [0061] “Fast Pick will direct the warehouse worker to those warehouse locations that have enough quantity to meet the desired pick quantity”), or
identifying dangerous operating conditions within the warehouse.
Regarding claim 6, the combination of Barnes, Walsh, and Lem teaches all the limitations of the base claims as outlined above.
Barnes further teaches wherein:
the data conversion rules include a device template configured to identify one or more fields of information included in a particular first sensor input signal obtained from a particular device type (Par. [0063] “computer system 132 may compute a context of the data. Moreover, the context may include: a location of electronic device 132 (which may be specified by or may correspond to GPS or local positioning system coordinates, a port, a network identifier, a VLAN identifier, etc.) a type of the data (such as environmental data, video, sound, etc.), a type of the electronic device (such as a video camera, a temperature sensor, a humidity sensor, a microphone, etc.), the first format, and/or a gateway (such as access point 116-1 or radio node 118-1 in a network) that forwards the data from electronic device 110-1 to computer system 132.; Par. [0097] “Moreover, based at least in part on the context, the computer system may identify the electronic device (operation 714) associated with the data. Note that the identifying may be based at least in part on: an identifier of the electronic device (such as a MAC address, an IMSI number, etc.), a subset of the data (such as a payload in a packet or a frame), a name of the electronic device, and/or additional data associated with a second electronic device (which is different from the electronic device).” – Identifying the electronic device associated with the contextual data would require rules or a template associated with a particular device.);
and the device template is used to convert the first sensor input signal to the second sensor input signal (Par. [0065] “computer system 132 may translate, based at least in part on identified electronic device 110-1, the data from the first format to a second format” – Examiner interprets performing a data conversion based on an identified electronic device as using a device template).
Regarding claim 7, the combination of Barnes, Walsh, and Lem teaches all the limitations of the base claims as outlined above.
Lem further teaches obtaining warehouse operations metadata, wherein controlling the one or more operations of the warehouse is based on the instruction to actuate warehouse operations and the warehouse operations metadata (Fig. 1-2, Par. [0054] “the present invention tracks "priorities" as initially defined in the initial environment as well as the delta between tasks done and to be done, where people are and what tasks they are performing, and if they are eligible to be re-assigned a certain task(s). The system of the present invention can also respond to a change in the tasks assigned or in priorities, push out the new task(s), be reactive to the environment, create tasks and re-allocate tasks based on existing or future priorities”; Par. [0059] “The embodiments of the present invention are not merely tracking product but priorities and changing them dynamically based on the changing environment thus optimizing labour, materials, costs, etc.”).
Regarding claim 8, the combination of Barnes, Walsh, and Lem teaches all the limitations of the base claims as outlined above.
Lem further teaches wherein the warehouse operations metadata includes:
operational error rates of warehouse operators (Par. [0050] “there is provided TED 301 which interacts with and can modify work priorities 302, task interleaving 303, feedback and performance tracking 304 and pace control 305.”. Examiner interprets performance tracking to include operational error rates of warehouse operators.)
time taken by warehouse operators to complete operations (Par. [0063] “the scheduled activity is monitored and the determination is made whether it is considered on track or off track”),
identities of the warehouse operators (Par. [0047] “"task engine distributor" ("TED"), which is an intelligence engine dynamically reallocating work tasks based on past, present or future properties, such as worker profile (certification, skill level, equipment, current activity), their location in the facility, and changing priorities that happen over the course of an assigned task or day.” – examiner interprets a worker profile as an identity; Par. [0057] “TED may identify to whom should be given the work to because warehouses can be quite large and there is a need to know where people are prior to making any task allocations.”), or
time between completion of the warehouse operations by the warehouse operators (Par. [0054] “tracks … delta between tasks done and to be done” – examiner interprets delta to include time between completion of warehouse operations).
Regarding claim 9, Barnes teaches one or more non-transitory computer-readable storage media configured to store instructions that, in response to being executed, cause a system to perform (Claim 13, “non-transitory computer-readable storage medium for use in conjunction with a computer system, the computer- readable storage medium storing program instructions, wherein, when executed by the computer system, cause the computer system to perform one or more operations”) industrial internet of things (IIOT) data conversion and distribution operations (Fig. 1, Par. [0007] “computer system translates, based at least in part on the identified electronic device, the data from the first format to a second format, where the second format is common to additional data associated with multiple different types of electronic devices, and where the additional data is stored in or is associated with the computer system.”; Par. [0061] “Access points 116, radio nodes 118 and/or services manager 130 may: manage network across different physical layers, provide sensor-to-backend management, and/or distributed decision-making.”; Par. [0047] “By translating the data to the second format, these communication techniques may address current obstacles and enable IoT applications and services”), the operations comprising:
obtaining a first sensor input signal from an IIOT device (Par. [0007] “the computer
system obtains the data associated with the electronic device, which has a first format.”; Par. [0063] “type of the electronic device (such as a video camera, a temperature sensor, a humidity sensor, a microphone, etc.)”);
converting the first sensor input signal to a second sensor input signal having a common data format based on one or more data conversion rules (Par. [0046] “computer system may translate, based at least in part on the identified electronic device, the data from the first format to a second format, where the second format is common to additional data associated with multiple different types of electronic devices” – basing the translation at least in part on the identified electronic device implies there are data conversion rules depending on the device type; Par. [0047] “This common format may enable data aggregation, analysis, decision-making and, thus, a wide variety of applications without requiring a standard, retrofitting of legacy equipment or the use of dedicated equipment”);
appending the second sensor input signal with a variable that describes information relating to the first sensor input signal, wherein the variable identifies a characteristic associated with the sensor data (Par. [0063] “computer system 132 may compute a context of the data. Moreover, the context may include: a location of electronic device 132 (which may be specified by or may correspond to GPS or local positioning system coordinates, a port, a network identifier, a VLAN identifier, etc.) a type of the data (such as environmental data, video, sound, etc.), a type of the electronic device (such as a video camera, a temperature sensor, a humidity sensor, a microphone, etc.), the first format, and/or a gateway (such as access point 116-1 or radio node 118-1 in a network) that forwards the data from electronic device 110-1 to computer system 132.” – one of ordinary skill in the art would understand that computing a context of the data would result in having additional useful data that would normally be appended to a data set for potential downstream use.);
Barnes does not explicitly teach broadcasting the second sensor input signal in the common data format to a particular data storage location, wherein:
the broadcasting the second sensor input signal includes communicating, using a public application programming interface (API) set, to the particular storage location included in a data storage according to the characteristic associated with the sensor data, and
the particular storage location is established for a receiving system to listen for the second sensor input signal from the HOT device;
responsive to detection by the receiving system of the second sensor input signal at the particular storage location, enabling access of the second sensor input signal by the receiving system;
sending an instruction to actuate a warehouse operation to the receiving systems based on the second sensor input signal accessed at the particular data storage location; and
controlling one or more operations of a warehouse based on the instruction to actuate the warehouse operation.
However, Walsh teaches broadcasting the second sensor input signal in the common data format to a particular data storage location (Fig. 14-15, Par. [0005], “receiving at a message collector module one or more messages formed by the platform gateway module; transmitting from the message collector module, the received one or more messages to a platform persistence module for storage”; Par. [0062], “The Persistence module 235 is the data store for each app. Each platform application, at provisioning / data modeling time, can decide what kind of persistent store they would like, such as an RDBMS, NoSql, newSQL, columnar, graphical, etc.”), wherein:
the broadcasting the second sensor input signal includes communicating, using a public application programming interface (API) set (Par. [0053], “Platform module 104 services can be accessed by Applications module 106 either by invoking the public interfaces using remote procedure call (RPC) or by calling web services of the platform”; Par. [0063], “Platform module 104 services can be accessed by Applications module 106 either by invoking the public interfaces using remote procedure call (RPC) or by calling web services of the platform. It is the external end point to be used by all to access the functionality within App SDK.”), to the particular storage location included in a data storage according to the characteristic associated with the sensor data (Par. [0042], “transforming the resource data to a Platform-specific format”; Par. [0062], “The Persistence module 235 is the data store for each app. Each platform application, at provisioning / data modeling time, can decide what kind of persistent store they would like, such as an RDBMS, NoSql, newSQL, columnar, graphical, etc.”; Par. [0007], “determining at the platform gateway module, a type of resource for the received data; and transforming at the platform gateway module, the received data into the message based on the determined type of resource, where the message is a format usable by the message collector module.”– resource type is interpreted as a characteristic associated with the sensor/resource data, and Walsh uses that characteristic to transform the data into a platform message.), and
the particular storage location is established for a receiving system to listen for the second sensor input signal from the IIOT device (Par. [0038], “The Persistence module 235 is the data store for each app. Each platform application, at provisioning / data modeling time, can decide what kind of persistent store they would like, such as an RDBMS, NoSql, newSQL, columnar, graphical, etc.”; Par. [0045], “Devices (more generally Resources), including Sensors (''S") and Actuators ("A") and humans or other organisms (not shown), are connected in a wired or wireless fashion (or via another Device) to Controllers. Sensors detect or measure some condition, for example temperature or the presence or absence of some chemical, and produce a signal”; Par. [0042], “transforming the resource data to a Platform-specific format”; Par. [0053], “Applications module 106 can subscribe to events produced by the Platform module 104 so that they receive those events as and when generated.”; Par. [0056], “The Message Listener module 221 listens for events from the Platform Core module 210, and then routes the message to the correct workflow” – The resource data after transformation into a platform specific message corresponds to the second sensor input signal, and the message listening module correspond to the receiving system listening for the second sensor input signal.); and
responsive to detection by the receiving system of the second sensor input signal at the particular storage location (Par. [0008], “receiving at a message listener module, the one or more events; and determining at the message listener module, a next step in a workflow based on the received event.”; Par. [0056], “The Message Listener module 221 listens for events from the Platform Core module 210, and then routes the message to the correct workflow”), enabling access of the second sensor input signal by the receiving system (Par. [0063], “The App Platform API module 237 is all of the workflows and persistence data stored … The way for an App (239, 241, 243) to interact with the Platform where the authentication, authorization, and the how is already baked into the App SDK. … The Apps (239, 241, 243) may access the Persistence module 235 to obtain needed information for a workflow.”).
Barnes and Walsh are analogous art because they are from the same field of endeavor and contain functional similarities. They both relate to internet of things data processing and distribution.
Therefore, at the time of effective filing date, it would have been obvious to a person of ordinary skill in the art to modify the above data conversion and distribution method, as taught by Barnes, and incorporate broadcasting using a public API, and workflow architecture to provide standardized access to converted IIOT data, app-specific storage, event subscription, message listening, and workflow routing, as taught by Walsh.
One of ordinary skill in the art would have been motivated to efficiency of IoT applications, enabling both a rapid time to market and regular improvements, as suggested by Walsh (Par. [0078]).
Barnes and Walsh do not explicitly teach sending an instruction to actuate a warehouse operation to the receiving systems based on the second sensor input signal accessed at the particular data storage location; and
controlling one or more operations of a warehouse based on the instruction to actuate the warehouse operation.
However, Lem teaches sending an instruction to actuate a warehouse operation to the receiving systems based on the second sensor input signal accessed at the particular data storage location (Fig. 1, Par. [0012] “system for managing tasks during the operation of a warehouse, the system comprising: (a) setting at least one initial parameter for the operation of the warehouse; (b) setting at least one priority during the operation of the warehouse; (c) commencing and monitoring the actual operation of the warehouse; (d) comparing the actual operation of the ware house compared with the at least one priority and the at least one initial parameter to determine whether the operation of the warehouse is considered on track or off track; and (e) taking remedial steps based on the comparison from step (d).”); and
controlling one or more operations of a warehouse based on the instruction to actuate the warehouse operation. (Par. [0047] “"task engine distributor" ("TED"), which is an intelligence engine dynamically reallocating work tasks based on past, present or future properties, such as worker profile (certification, skill level, equipment, current activity), their location in the facility, and changing priorities that happen over the course of an assigned task or day.”).
Barnes, Walsh, and Lem are analogous art because they are from the same field of endeavor. They relate to industrial internet of things.
Therefore, at the time of effective filing date, it would have been obvious to a person of ordinary skill in the art to modify the above data conversion, distribution method, and workflow system, as taught by Barnes and Walsh, and incorporate automated warehouse workflow control, as taught by Lem.
One of ordinary skill in the art would have been motivated to improve “automated workflows that can adjust based on situational awareness” as suggested by Lem (Par. [0009]).
Regarding claim 12, the combination of Barnes, Walsh, and Lem teaches all the limitations of the base claims as outlined above.
Lem further teaches wherein controlling the one or more operations of the warehouse
include:
monitoring behavior of one or more warehouse operators (Par. [0054] “the present invention tracks “priorities” as initially defined in the initial environment as well as the delta between tasks done and to be done, where people are and what tasks they are performing”),
specifying an item to be retrieved from the warehouse (Par. [0060] “a number of different pick options including Pick faces Only, FIFO or LIFO, Fast Pick, Stable Pallet, Minimize Honeycomb, Minimize Travel, Output Level. Warehouse Operational options may include Pallet Cube Size, Serpentine YIN, Labour Units, Standby Labor, Hrs per Shift and other production benchmarks.”),
specifying a quantity of the item to be retrieved from the warehouse (Par. [0061] “Fast Pick will direct the warehouse worker to those warehouse locations that have enough quantity to meet the desired pick quantity”), or
identifying dangerous operating conditions within the warehouse.
Regarding claim 14, the combination of Barnes, Walsh, and Lem teaches all the limitations of the base claims as outlined above.
Barnes further teaches wherein:
the data conversion rules include a device template configured to identify one or more fields of information included in a particular first sensor input signal obtained from a particular device type Par. [0063] “computer system 132 may compute a context of the data. Moreover, the context may include: a location of electronic device 132 (which may be specified by or may correspond to GPS or local positioning system coordinates, a port, a network identifier, a VLAN identifier, etc.) a type of the data (such as environmental data, video, sound, etc.), a type of the electronic device (such as a video camera, a temperature sensor, a humidity sensor, a microphone, etc.), the first format, and/or a gateway (such as access point 116-1 or radio node 118-1 in a network) that forwards the data from electronic device 110-1 to computer system 132.; Par. [0097] “Moreover, based at least in part on the context, the computer system may identify the electronic device (operation 714) associated with the data. Note that the identifying may be based at least in part on: an identifier of the electronic device (such as a MAC address, an IMSI number, etc.), a subset of the data (such as a payload in a packet or a frame), a name of the electronic device, and/or additional data associated with a second electronic device (which is different from the electronic device).” – Identifying the electronic device associated with the contextual data would require rules or a template associated with a particular device.); and
the device template is used to convert the first sensor input signal to the second sensor input signal (Par. [0065] “computer system 132 may translate, based at least in part on identified electronic device 110-1, the data from the first format to a second format” – Examiner interprets performing a data conversion based on an identified electronic device as using a device template).
Regarding claim 15, the combination of Barnes, Walsh, and Lem teaches all the limitations of the base claims as outlined above.
Lem further teaches wherein:
the operations further comprise obtaining warehouse operations metadata (Fig. 1-2, Par. [0054] “the present invention tracks "priorities" as initially defined in the initial environment as well as the delta between tasks done and to be done, where people are and what tasks they are performing, and if they are eligible to be re-assigned a certain task(s)”; Par. [0059] “The embodiments of the present invention are not merely tracking product but priorities and changing them dynamically based on the changing environment thus optimizing labour, materials, costs, etc.”); and
the controlling the one or more operations of the warehouse is based on the instruction to actuate warehouse operations and the warehouse operations metadata (Fig. 1-2, Par. [0054] The system of the present invention can also respond to a change in the tasks assigned or in priorities, push out the new task(s), be reactive to the environment, create tasks and re-allocate tasks based on existing or future priorities”).
Regarding claim 16, the combination of Barnes, Walsh, and Lem teaches all the limitations of the base claims as outlined above.
Lem further teaches wherein the warehouse operations metadata includes:
operational error rates of warehouse operators (Par. [0050] “there is provided TED 301 which interacts with and can modify work priorities 302, task interleaving 303, feedback and performance tracking 304 and pace control 305”. - Examiner interprets performance tracking to include operational error rates of warehouse operators.)
time taken by warehouse operators to complete operations (Par. [0063] “the scheduled activity is monitored and the determination is made whether it is considered on track or off track”),
identities of the warehouse operators (Par. [0047] “"task engine distributor" ("TED"), which is an intelligence engine dynamically reallocating work tasks based on past, present or future properties, such as worker profile (certification, skill level, equipment, current activity), their location in the facility, and changing priorities that happen over the course of an assigned task or day.” – examiner interprets a worker profile as an identity; Par. [0057] “TED may identify to whom should be given the work to because warehouses can be quite large and there is a need to know where people are prior to making any task allocations.”), or
time between completion of the warehouse operations by the warehouse operators (Par. [0054] “tracks … delta between tasks done and to be done” – examiner interprets delta to include time between completion of warehouse operations).
Claim(s) 2, 5, 10, 13 is/are rejected under 35 U.S.C. 103 as being unpatentable over Barnes USPGPUB 2023/0300623 A1 (hereinafter Barnes) in view of Walsh et al. USPGPUB 2023/0319158 A1 (hereinafter Walsh) and Lem USPGPUB 2019/0303819 A1 (hereinafter Lem), and further in view of Cella et al. USPGPUB 2020/0348662 A1 (hereinafter Cella).
Regarding claim 2, the combination of Barnes, Walsh, and Lem teaches all the limitations of the base claims as outlined above.
Barnes, Walsh, and Lem do not explicitly teach wherein obtaining the first sensor input signal includes:
applying a data conversion rule of a plurality of data conversion rules to the first sensor input signal in which each data conversion rule of the plurality converts a particular sensor input signal with a corresponding data format to the common data form.
However, Cella teaches wherein obtaining the first sensor input signal includes:
applying a data conversion rule of a plurality of data conversion rules to the first sensor input signal in which each data conversion rule of the plurality converts a particular sensor input signal with a corresponding data format to the common data format (Fig. 18, Par. [0712] “include a streaming data collector 4510 that may be configured to accept data in a range of formats as described herein. In embodiments, the range of formats can include a data format A 4520, a data format B 4522, a data format C 4524, and a data format D 4528 that may be sourced from a range of sensors”; Par. [0713] “extracted data 4650 that can include extracted portions of translated legacy data 4652 and streamed data 4654 may be stored in a format that facilitates access and processing by legacy instrument data processing and further processing that can emulate legacy instrument data processing methods, and the like.” – depending on the data format from a specific sensor, different data conversion rules must be applied in order to create a common data format).
Barnes, Walsh, Lem, and Cella are analogous art because they are from the same field of endeavor and contain functional similarities. They all relate to industrial internet of things.
Therefore, at the time of effective filing date, it would have been obvious to a person of ordinary skill in the art to modify the above IIoT warehouse control system, as taught by Barnes, Walsh, and Lem, and incorporate a plurality of format sensor conversion teaching in order to allow the system to receive sensor data from multiple sensor formats and convert the data into a common format for storage, access, analysis, and workflow processing, as taught by Cella.
One of ordinary skill in the art would have been motivated to improve allowing existing processing systems and facilities to access and process data as suggested by Cella (Par. [0531]).
Regarding claim 5, the combination of Barnes, Walsh, and Lem teaches all the limitations of the base claims as outlined above.
Barnes, Walsh, and Lem do not explicitly teach wherein obtaining the first sensor input signal includes:
receiving, by a machine-learning system, information relating to warehouse operations; and
generating, by the machine-learning system, a virtual sensor input signal responsive to the information relating to warehouse operations.
However, Cella teaches wherein obtaining the first sensor input signal includes:
receiving, by a machine-learning system, information relating to warehouse operations (Fig. 1-6, Par. [0207] “adaptive intelligent systems layer includes data processing, artificial intelligence, and computational systems that develop, improve, or adapt processes in the IIoT system based on the data collected by the industrial monitoring systems layer.”; Par. [0539] “Intelligent systems may include machine learning systems 122, such as for learning on one or more data sets. The one or more data sets may include information collected using local data collection systems 102 or other information from input sources 116, such as to recognize states, objects, events, patterns, conditions, or the like”); and
generating, by the machine-learning system, a virtual sensor input signal responsive to the information relating to warehouse operations (Par. [0539] “The one or more data sets may include information collected using local data collection systems 102 or other information from input sources 116, such as to recognize states, objects, events, patterns, conditions, or the like that may, in turn, be used for processing by the host system 112 as inputs to components of the platform 100 and portions of the industrial IoT data collection, monitoring and control system 10, or the like”).
Barnes, Walsh, Lem, and Cella are analogous art because they are from the same field of endeavor and contain functional similarities. They all relate to industrial internet of things.
Therefore, at the time of effective filing date, it would have been obvious to a person of ordinary skill in the art to modify the above IIoT warehouse control system, as taught by Barnes, Walsh, and Lem, and incorporate machine learning analysis of industrial monitoring data in order to generate inferred states, events, or conditions as virtual sensor inputs for downstream warehouse monitoring and control, as taught by Cella.
One of ordinary skill in the art would have been motivated to improve allowing existing processing systems and facilities to access and process data as suggested by Cella (Par. [0531]).
Regarding claim 10, the combination of Barnes, Walsh, and Lem teaches all the limitations of the base claims as outlined above.
Barnes, Walsh, and Lem do not explicitly teach wherein obtaining the first sensor input signal includes:
applying a data conversion rule of a plurality of data conversion rules to the first sensor input signal in which each data conversion rule of the plurality converts a particular sensor input signal with a corresponding data format to the common data form.
However, Cella teaches wherein obtaining the first sensor input signal includes:
applying a data conversion rule of a plurality of data conversion rules to the first sensor input signal in which each data conversion rule of the plurality converts a particular sensor input signal with a corresponding data format to the common data format (Fig. 18, Par. [0712] “include a streaming data collector 4510 that may be configured to accept data in a range of formats as described herein. In embodiments, the range of formats can include a data format A 4520, a data format B 4522, a data format C 4524, and a data format D 4528 that may be sourced from a range of sensors”; Par. [0713] “extracted data 4650 that can include extracted portions of translated legacy data 4652 and streamed data 4654 may be stored in a format that facilitates access and processing by legacy instrument data processing and further processing that can emulate legacy instrument data processing methods, and the like.” – depending on the data format from a specific sensor, different data conversion rules must be applied in order to create a common data format).
Barnes, Walsh, Lem, and Cella are analogous art because they are from the same field of endeavor and contain functional similarities. They all relate to industrial internet of things.
Therefore, at the time of effective filing date, it would have been obvious to a person of ordinary skill in the art to modify the above IIoT warehouse control system, as taught by Barnes, Walsh, and Lem, and incorporate a plurality of format sensor conversion teaching in order to allow the system to receive sensor data from multiple sensor formats and convert the data into a common format for storage, access, analysis, and workflow processing, as taught by Cella.
One of ordinary skill in the art would have been motivated to improve allowing existing processing systems and facilities to access and process data as suggested by Cella (Par. [0531]).
Regarding claim 13, the combination of Barnes, Walsh, and Lem teaches all the limitations of the base claims as outlined above.
Barnes, Walsh, and Lem do not explicitly teach wherein obtaining the first sensor input signal includes:
receiving, by a machine-learning system, information relating to warehouse operations; and
generating, by the machine-learning system, a virtual sensor input signal responsive to the information relating to warehouse operations.
However, Cella teaches wherein obtaining the first sensor input signal includes:
receiving, by a machine-learning system, information relating to warehouse operations (Fig. 1-6, Par. [0207] “adaptive intelligent systems layer includes data processing, artificial intelligence, and computational systems that develop, improve, or adapt processes in the IIoT system based on the data collected by the industrial monitoring systems layer.”; Par. [0539] “Intelligent systems may include machine learning systems 122, such as for learning on one or more data sets. The one or more data sets may include information collected using local data collection systems 102 or other information from input sources 116, such as to recognize states, objects, events, patterns, conditions, or the like”); and
generating, by the machine-learning system, a virtual sensor input signal responsive to the information relating to warehouse operations (Par. [0539] “The one or more data sets may include information collected using local data collection systems 102 or other information from input sources 116, such as to recognize states, objects, events, patterns, conditions, or the like that may, in turn, be used for processing by the host system 112 as inputs to components of the platform 100 and portions of the industrial IoT data collection, monitoring and control system 10, or the like”).
Barnes, Walsh, Lem, and Cella are analogous art because they are from the same field of endeavor and contain functional similarities. They all relate to industrial internet of things.
Therefore, at the time of effective filing date, it would have been obvious to a person of ordinary skill in the art to modify the above IIoT warehouse control system, as taught by Barnes, Walsh, and Lem, and incorporate machine learning analysis of industrial monitoring data in order to generate inferred states, events, or conditions as virtual sensor inputs for downstream warehouse monitoring and control, as taught by Cella.
One of ordinary skill in the art would have been motivated to improve allowing existing processing systems and facilities to access and process data as suggested by Cella (Par. [0531]).
Citation of Pertinent Prior Art
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Dixon [USPGPUB 2024/0019852 A1] teaches a method for cloud edge network process automation control that includes receiving, at a multi-access edge computing device, a factory application defining an automated process; and identifying process modules associated with the factory application. The method further includes providing distributed orchestration information to the devices, where the distributed orchestration information includes a status of the process modules; and controlling networked plant devices for middle latency conditions based upon the distributed orchestration information.
Okamoto et al. [USPGPUB 2022/0155759 A1] teaches an interface apparatus including an interface unit configured to receive, from an AI processing unit configured to execute at least either processing of generating a model for determining a state of a facility by machine learning or processing of determining the state of the facility using the model, a first command using a protocol of a common form that is not dependent on a cloud platform configured to manage the facility, and a cloud communication unit configured to convert the first command into a second command.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to PETER XU whose telephone number is (571)272-0792. The examiner can normally be reached Monday-Friday 9am-5pm.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Mohammad Ali can be reached at (571) 272-4105. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/PETER XU/ Examiner, Art Unit 2119
/MOHAMMAD ALI/Supervisory Patent Examiner, Art Unit 2119
Prosecution Insights