DETAILED ACTION
The instant application having Application No. 19/040,586 has a total of 16 claims pending in the application; there are 2 independent claims and 14 dependent claims, all of which are ready for examination by the examiner.
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 .
Priority
Acknowledgment is made of applicant’s claim for foreign priority under 35 U.S.C. 119 (a)-(d).
INFORMATION CONCERNING DRAWINGS
Drawings
The drawings are objected to as failing to comply with 37 CFR 1.84(p)(5) because they include the following reference character(s) not mentioned in the description: 10 in FIG. 1-3 and 108 in FIG. 1. Corrected drawing sheets in compliance with 37 CFR 1.121(d), or amendment to the specification to add the reference character(s) in the description in compliance with 37 CFR 1.121(b) are required in reply to the Office action to avoid abandonment of the application. Any amended replacement drawing sheet should include all of the figures appearing on the immediate prior version of the sheet, even if only one figure is being amended. Each drawing sheet submitted after the filing date of an application must be labeled in the top margin as either “Replacement Sheet” or “New Sheet” pursuant to 37 CFR 1.121(d). If the changes are not accepted by the examiner, the applicant will be notified and informed of any required corrective action in the next Office action. The objection to the drawings will not be held in abeyance.
INFORMATION CONCERNING THE SPECIFICATION
Specification
The applicant’s specification submitted is acceptable for examination purposes.
REJECTIONS BASED ON PRIOR ART
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103(a) which forms the basis for all obviousness rejections set forth in this Office action:
(a) A patent may not be obtained though the invention is not identically disclosed or described as set forth in section 102 of this title, if the differences between the subject matter sought to be patented and the prior art are such that the subject matter as a whole would have been obvious at the time the invention was made to a person having ordinary skill in the art to which said subject matter pertains. Patentability shall not be negatived by the manner in which the invention was made.
Claims 1-16 are rejected under 35 U.S.C. 103(a) as being unpatentable over Zhang et al. (Publication Number US 2020/0117614 A1) in view of Thimmanaik et al. (Publication Number US 2022/0066435 A1) and Haynes (Patent Number US 11,958,410 B2).
As per claim 1, Zhang et al. discloses “A coordinated control system designed with a small form factor that can be located within a robot or an automated inline manufacturing system (computation device [Paragraph 0006] within a device such as an electronic device that includes a robot; Paragraphs 0038-0039 and 0326), comprising an on board Advanced Distributed Control Hardware (ADCH) configured with Artificial intelligence enabled processors incorporated within a System on Module (SOM) board (processing device that include machine learning operation device; Paragraphs 0027 and 0312-0317), each SOM comprising four processors or nodes (the idea of four processors is similar to four DDR4 controllers within a chip; Paragraph 0328).”
Zhang et al. discloses “the ADCH electrically connected to a plurality of sensors for perceiving the environment and collecting data from infrared, tactile, proximity and other types of sensors (sensors and cameras; Paragraph 0039, 0318, and 0332).”
However, Zhang et al. does not disclose “the ADCH interfaced with a camera, a motor, a general purpose I/O through I2C expander, an audio interface, the Internet, and wireless communication transmitter and receiver to send and receive commands and any other data,” “the ADCH in data communication with an external host flashing computer dedicated for uploading/downloading firmware, cloning, and configuration setup,” or “the ADCH comprising a non-volatile memory for storing control algorithms, configuration files, and other essential data.” Thimmanaik et al. discloses “the ADCH interfaced with a camera, a motor, a general purpose I/O through I2C expander, an audio interface, the Internet, and wireless communication transmitter and receiver to send and receive commands and any other data (note that Zhang et al. discloses audio and visual inputs/outputs in the form of cameras and headphones [Paragraph 0332] while Thimmanaik et al. is directed to the I2C [Paragraph 0168] and networks; Paragraph 0169).” Thimmanaik et al. discloses “the ADCH in data communication with an external host flashing computer dedicated for uploading/downloading firmware, cloning, and configuration setup (instructions in the form of software/firmware [Paragraphs 0159 and 0180] of which there is a software distribution platform providing patches and updates; Paragraph 0184).”
Thimmanaik et al. discloses “the ADCH comprising a non-volatile memory for storing control algorithms, configuration files, and other essential data (non-volatile memory; Paragraph 0156).”
Zhang et al. and Thimmanaik et al. are analogous art in that they disclose the use of machine-learning/artificial intelligence.
Before the effective filing date of the claimed invention it would have been obvious to a person of ordinary skill in the art to combine the elements of Zhang et al. and Thimmanaik et al. for use in scaling automation [Paragraph 0004].
However, Zhang et al. and Thimmanaik et al. do not disclose “an HDMI-based user interface in data communication with the ADCH, the HDMI-based user interface for robot operation, training, and setup,” “an USB C- or Ethernet-based internal star network in data communication with the ADCH, the HDMI-based user interface for high-speed communication bypassing the standard PCI bus interface bus,” or “and an Ethernet switch board in data communication with the ADCH, the HDMI-based user interface enabling multiple boards to access the Ethernet for both internal communication within the star network and external Internet access.” Haynes discloses “an HDMI-based user interface in data communication with the ADCH, the HDMI-based user interface for robot operation, training, and setup (Haynes discloses HDMI [Column 5, lines 4-14] in a system includes the training and use of machine-learning; FIG. 15).” Haynes discloses “an USB C- or Ethernet-based internal star network in data communication with the ADCH (Column 8, lines 7-15), the HDMI-based user interface for high-speed communication bypassing the standard PCI bus interface bus (while Thimmanaik et al. discloses PCI in [Paragraph 0168], Haynes discloses HDMI in [Column 5, lines 4-14]).” Haynes discloses “and an Ethernet switch board in data communication with the ADCH, the HDMI-based user interface enabling multiple boards to access the Ethernet for both internal communication within the star network and external Internet access (Column 5, lines 4-14; Column 8, lines 7-15; Column 19, lines 39-48).”
Zhang et al., Thimmanaik et al., and Haynes are analogous art in that they disclose the use of machine-learning/artificial intelligence.
Before the effective filing date of the claimed invention it would have been obvious to a person of ordinary skill in the art to combine the elements of Zhang et al. and Thimmanaik et al. with elements of Haynes for highly responsive and adaptive system [Column 20, lines 57-64].
As per claim 2, Thimmanaik et al. discloses “The coordinated control system of claim 1 (as disclosed by Zhang et al., Thimmanaik et al., and Haynes above), further comprising: an artificial intelligence enabled system for processing data locally in the ADCH enabling a significant reduction in latency to make autonomous decisions supported by artificial intelligence-based machine learning and deep learning algorithms (the high-resolution images may be provided to processing circuitry (e.g., internal or integral to the robot) to perform machine-learning techniques on the images to detect or predict the occurrence of anomalies in the industrial process in near real time; Paragraph 0041).”
Thimmanaik et al. discloses “ wherein the ADCH comprises multiple processors each consisting of a Graphic Processing Unit (GPU), an ARM-based Central Processing Unit (CPU), a Vision Processing Accelerator (VPA), and a Deep Learning Accelerator (DLA) for analysing the environment and achieving real-time performance (AI hardware such as GPUs or programmed FPGAs; Paragraph 0155).” Haynes discloses “and a user interface for robot interaction, wherein a HDMI-based user interface comprises a display, a mouse for both visual and voice communication with human operators (HDMI used for a range of applications such as data transfer, video output, or audio input/output; Column 5, lines 10-14).”
As per claim 3, Thimmanaik et al. discloses “The coordinated control system of claim 1 (as disclosed by Zhang et al., Thimmanaik et al., and Haynes above), further comprising: a power management feature within the ADCH to optimise power or battery usage by managing processors and disabling unused processor clocks to ensure low thermal dissipation (through a battery monitor/charger; Paragraph 0178-0179).”
As per claim 4, Thimmanaik et al. discloses “The coordinated control system of claim 1 (as disclosed by Zhang et al., Thimmanaik et al., and Haynes above), further comprising: a self-diagnostic system to detect and report malfunctions in real-time, within the computer, and to detect and report faulty or inappropriate responses from the external hardware interfaces (as it pertains to a problem area and anomaly detection; Paragraph 0081).”
Zhang et al. also discloses the above limitation as “a self-diagnostic system to detect and report malfunctions in real-time, within the computer, and to detect and report faulty or inappropriate responses from the external hardware interfaces (error checking and correction; Paragraph 0328).”
As per claim 5, Thimmanaik et al. discloses “The coordinated control system of claim 1 (as disclosed by Zhang et al., Thimmanaik et al., and Haynes above), further comprising: data security and privacy features that enable processing and storing data locally in the non-volatile memory, without the risk of data exposure during transmission, which can be crucial for sensitive applications (improved security of hard and root of trust; Paragraph 0146).”
As per claim 6, Thimmanaik et al. discloses “The coordinated control system of claim 1 (as disclosed by Zhang et al., Thimmanaik et al., and Haynes above), further comprising: a hot flashing computer in data communication with the ADHC, the hot flashing computer dedicated to modify an operating system kernel of multiple processors in the ACDH to customise a software application, a bootloader, and device drivers aiding in scalability and flexibility (one or more servers of the software distribution platform 1505 are communicatively connected to one or more security domains and/or security devices through which requests and transmissions of the example computer readable instructions 1482 must pass. In some examples, one or more servers of the software distribution platform 1505 periodically offer, transmit, and/or force updates to the software (e.g., the example computer readable instructions 1482 of FIG. 14B) to ensure improvements, patches, updates, etc. are distributed and applied to the software at the end user devices; Paragraph 0184).”
As per claim 7, Zhang et al. discloses “The coordinated control system of claim 1 (as disclosed by Zhang et al., Thimmanaik et al., and Haynes above), wherein the ADCH enables a distributed and self-contained robotic environment which is scalable and adaptable to new applications (primary processing circuit that cascades through nodes to secondary processing circuits; FIG. 3F).”
As per claim 8, Zhang et al. discloses “A coordinated control method for a robot or an automated inline system in a manufacturing environment (computation device [Paragraph 0006] within a device such as an electronic device that includes a robot; Paragraphs 0038-0039 and 0326), the method comprising: utilizing a built-in computer comprising Advanced Distributed Control Hardware (ADCH) to coordinate and execute tasks related to a production process, including material handling and quality control (processing device that include machine learning operation device; Paragraphs 0027 and 0312-0317).”
Zhang et al. discloses “managing synchronous service calls implemented through short duration remote service calls which are executed sequentially, staying dedicated and active during its execution, and not being preemptible by another remote service call (using queues where the instructions are executed in sequence; Paragraph 0108).”
Zhang et al. discloses “selectively scaling the functionality and speed of the ADCH by reassigning unused nodes or processors aiding scalability and flexibility (primary processing circuit that cascades through nodes to secondary processing circuits; FIG. 3F).”
Zhang et al. discloses “dynamically allocating nodes or processors by the master node, to distribute the tasks efficiently for maximum computing speed during data and image analysis during normal operation, debugging and user interface utilisation during training and configuration (primary processing circuit that cascades through nodes to secondary processing circuits; FIG. 3F).”
However, Zhang et al. does not disclose “and establishing tight coupling of two or more processes by action servers and clients to run different tasks by the assigned server, with the option to provide feedback during and at the end of execution of a remote service call or a service request from a client and wherein action servers are designed to be preemptable and non-blocking, enabling them to execute multiple tasks with password protected data security and privacy features within the ADCH to process and store data locally, without the risk of data exposure during transmission to external servers when used in sensitive applications.” Thimmanaik et al. discloses “and establishing tight coupling of two or more processes by action servers and clients to run different tasks by the assigned server, with the option to provide feedback during and at the end of execution of a remote service call or a service request from a client and wherein action servers are designed to be preemptable and non-blocking (where an anomaly triggers a responsive action (can be interpreted as an interrupt); Paragraph 0134; FIG. 10), enabling them to execute multiple tasks with password protected data security and privacy features within the ADCH to process and store data locally, without the risk of data exposure during transmission to external servers when used in sensitive applications (improved security of hard and root of trust; Paragraph 0146).”
Zhang et al. and Thimmanaik et al. are analogous art in that they disclose the use of machine-learning/artificial intelligence.
Before the effective filing date of the claimed invention it would have been obvious to a person of ordinary skill in the art to combine the elements of Zhang et al. and Thimmanaik et al. for use in scaling automation [Paragraph 0004].
However, Zhang et al. and Thimmanaik et al. do not disclose “receiving external production instructions via WiFi, Bluetooth, or Ethernet from a central manufacturing control system or from a software application residing in a built-in computer,” “rapidly communicating within the ADCH through broadcasting messages that can be published by any process in the star network without any knowledge of the subscribers to the messages resulting in a typical many-to-many connection for continuous data flow,”or “communicating data over an established peer-to-peer network between processes running on each pair of processors.”
Haynes discloses “receiving external production instructions via WiFi, Bluetooth, or Ethernet from a central manufacturing control system or from a software application residing in a built-in computer (Column 3, lines 46-56).” Haynes discloses “rapidly communicating within the ADCH through broadcasting messages that can be published by any process in the star network without any knowledge of the subscribers to the messages resulting in a typical many-to-many connection for continuous data flow (such as in a peer-to-peer (or distributed) environment; Column 27, lines 47-63).” Haynes discloses “communicating data over an established peer-to-peer network between processes running on each pair of processors (such as in a peer-to-peer (or distributed) environment; Column 27, lines 47-63).”
Zhang et al., Thimmanaik et al., and Haynes are analogous art in that they disclose the use of machine-learning/artificial intelligence.
Before the effective filing date of the claimed invention it would have been obvious to a person of ordinary skill in the art to combine the elements of Zhang et al. and Thimmanaik et al. with elements of Haynes for highly responsive and adaptive system [Column 20, lines 57-64].
As per claim 9, Thimmanaik et al. discloses “The coordinated control method of claim 8 (as disclosed by Zhang et al., Thimmanaik et al., and Haynes above), wherein: processes are designed to be fine grained and modular to ensure each process performs a well-defined task with invokable interfaces (processing hardware may be modular such that it may be removably attached and incorporated in the robot in a modular manner; Paragraph 0114).”
As per claim 10, Haynes discloses “The coordinated control method of claim 9 (as disclosed by Zhang et al., Thimmanaik et al., and Haynes above), wherein the invokable interfaces are exposed to all communication modes (broadcast messages and remote service calls) by the process based on the requirement (Column 5, lines 4-14; Column 8, lines 7-15; Column 19, lines 39-48).”
As per claim 11, Thimmanaik et al. discloses “The coordinated control method of claim 8 (as disclosed by Zhang et al., Thimmanaik et al., and Haynes above), wherein the sequence of robot movements are analysed and calculated to manage the robot joint angles for maintaining balance (in the context of robotic joints [Paragraphs 0115-0120] in view of anomaly detection; Paragraph 0122).”
As per claim 12, Thimmanaik et al. discloses “The coordinated control method of claim 8 (as disclosed by Zhang et al., Thimmanaik et al., and Haynes above), wherein the trajectory paths of the robot are planned through implementation of algorithms for inverse kinematics to ensure smooth, non- jerky and accurate movements enabling implementation of anthropomorphic features (in the context of robotic joints; Paragraphs 0115-0120).”
As per claim 13, Thimmanaik et al. discloses “The coordinated control method of claim 8 (as disclosed by Zhang et al., Thimmanaik et al., and Haynes above), wherein pre-defined behaviours or movements are utilised to perform specific tasks for autonomous navigation of robots in an indoor and familiar environment (in the context of robotic joints; Paragraphs 0115-0120).”
As per claim 14, Haynes discloses “The coordinated control method of claim 8 (as disclosed by Zhang et al., Thimmanaik et al., and Haynes above), wherein effective Artificial Intelligence (Al) algorithms are implemented for object recognition, speech recognition, natural language processing and decision-making resulting in understanding a natural language speech command and generating an appropriate natural language response (Column 16, lines 11-17).”
As per claim 15, Thimmanaik et al. discloses “The coordinated control method of claim 8 (as disclosed by Zhang et al., Thimmanaik et al., and Haynes above), wherein the vision systems aid in understanding the manufacturing environment and quality inspection capabilities complimented by Artificial Intelligence (Al) inferencing, deep learning, and reinforced learning for an efficient end-to-end autonomous application (the high-resolution images may be provided to processing circuitry (e.g., internal or integral to the robot) to perform machine-learning techniques on the images to detect or predict the occurrence of anomalies in the industrial process in near real time; Paragraph 0041).”
As per claim 16, Haynes discloses “The coordinated control method of claim 8 (as disclosed by Zhang et al., Thimmanaik et al., and Haynes above), wherein efficient data transfer is facilitated through the Ethernet and USB star network and interfaces within the ADCH to enable distributed real-time image processing and inferencing across multiple nodes as well as to overcome common bottlenecks encountered in conventional multi-GPU server systems that rely on a shared bus architecture (Column 5, lines 4-14; Column 8, lines 7-15; Column 19, lines 39-48).”
RELEVENT ART CITED BY THE EXAMINER
The following prior art made of record and relied upon is citied to establish the level of skill in the applicant’s art and those arts considered reasonably pertinent to applicant’s disclosure. See MPEP 707.05(c).
The following references teach robotic with onboard machine learning/AI.
U.S. PATENT NUMBERS:2022/0133114 A1 – [Paragraph 0029]
2024/0053748 A1 – [Paragraph 0182]
12,045,059 B1 – [Claim 1]
CLOSING COMMENTS
Conclusion
The examiner requests, in response to this Office action, support be shown for language added to any original claims on amendment and any new claims. That is, indicate support for newly added claim language by specifically pointing to page(s) and line no(s) in the specification and/or drawing figure(s). This will assist the examiner in prosecuting the application.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Henry Yu whose telephone number is (571)272-9779. The examiner can normally be reached Monday - Friday.
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/H.W.Y/Examiner, Art Unit 2181 June 25, 2026
/IDRISS N ALROBAYE/Supervisory Patent Examiner, Art Unit 2181
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