Prosecution Insights
Last updated: April 19, 2026
Application No. 18/152,001

Integrated Sensor Device with Deep Learning Accelerator and Random Access Memory

Non-Final OA §103
Filed
Jan 09, 2023
Examiner
TSENG, CHENG YUAN
Art Unit
2615
Tech Center
2600 — Communications
Assignee
Micron Technology, Inc.
OA Round
5 (Non-Final)
84%
Grant Probability
Favorable
5-6
OA Rounds
2y 6m
To Grant
99%
With Interview

Examiner Intelligence

Grants 84% — above average
84%
Career Allow Rate
703 granted / 835 resolved
+22.2% vs TC avg
Strong +16% interview lift
Without
With
+15.7%
Interview Lift
resolved cases with interview
Typical timeline
2y 6m
Avg Prosecution
30 currently pending
Career history
865
Total Applications
across all art units

Statute-Specific Performance

§101
5.4%
-34.6% vs TC avg
§103
28.1%
-11.9% vs TC avg
§102
39.1%
-0.9% vs TC avg
§112
15.4%
-24.6% vs TC avg
Black line = Tech Center average estimate • Based on career data from 835 resolved cases

Office Action

§103
DETAILED ACTION Claim Rejections - 35 USC § 103 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. Claims 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over Desoli (US 2018/0,189,229) in view of Brown (US 10/838,837). Referring to claims 1, 10 and 14, Desoli discloses a device (fig. 3, device 100) comprising: an integrated circuit package (fig. 3, SoC 110) to enclose: a sensor (fig. 4, sensor interface 408b/408c); a memory (fig. 3, RAM 126); and a deep learning accelerator (fig. 3, CAF 400 and DSP 138; para.0134) configured to perform parallel vector (para.0283, vector) or matrix calculation (fig. 1A, convolution operation), the deep learning accelerator having: a memory interface (fig. 3, bus 166) directly connecting the memory to the deep learning accelerator; a control unit (fig. 3, memory control 174); one processing unit (fig. 4, convolution accelerator CA 600; fig. 6A) to execute instructions (fig. 6A, MAC units 620) having matrix operands (fig. 6A, data stream 606/604/602); and a host interface (fig. 3, AXI 132); wherein the deep learning accelerator is to: perform computations of an artificial neural network (fig. 1A, convolutional neural network) to generate an output (fig. 1A, CNN output) of the artificial neural network responsive to an input (fig. 1A, input image) from the sensor; and communicate, via the host interface (fig. 3, AXI 132), the output to a host system (fig. 3, CPU 128; para.0134, host application). Brown discloses a sensor formed on a substrate (fig. 2, integrated circuit 202/204/208; 6:33-50, on-chip) of the integrated circuit package including a temperature sensor (5:15-20, temperature sensor). Desoli and Brown are analogous art because they are from the same field of endeavor in implementing deep learning integrated circuit for vehicle applications (Desoli, para.0006, Brown, 6:1-32). At the time of the filing, it would have been obvious to a person of ordinary skill in the art, having the teaching of Desoli and Brown before him or her to modify the SoC 100 of Desoli to include the integrated on-chip sensor 202/204/208 of Brown, thereafter the deep learning SoC device has integrated acceleration sensor within single integrated circuit (Brown, para.0157). The suggestion and/or motivation for doing so would be obtaining the advantage of improved system size advantage as suggested by Brown. Therefore, it would have been obvious to combine Desoli with Brown to obtain the invention as specified in the application claims. As to claims 2 and 15, Desoli discloses the device of claim 1, wherein the memory is to store: first data (para.0145, weight) representative of weights of the artificial neural network; and second data representative of instructions (para.0145, activations) executable by the deep learning accelerator to implement the computations of the artificial neural network. As to claims 3, 11 and 16, Desoli discloses the device of claim 2, wherein the sensor is to write, into the memory, third data (para.0147, camera sensor data) representative of the input to the artificial neural network, and the control unit is to perform the computations of the artificial neural network using the first data and the second data in response to third data being written into the memory (para.0147, DCNN operations). As to claims 4-6 and 17, Desoli discloses the device of claim 3, wherein the host interface is on a memory/peripheral/serial communication bus (fig. 3, AXI serial bus 132). As to claims 7 and 18, Desoli discloses the device of claim 6, comprising a set of connectors to be connected to a printed circuit board, a cable, or a receptacle (fig. 3, low speed peripheral 130). As to claims 8, 12 and 19, Desoli discloses the device of claim 3, wherein the integrated circuit package is with an opening (fig. 4, sensor interface 408b/408c) for the sensor to receive stimuli (fig. 4, image data). As to claims 9, 13 and 20, Brown discloses the device of claim 3, wherein the sensor includes an infrared radiation sensor (5:15-20, infrared sensor). Contact Information Any inquiry concerning this communication or earlier communications from the examiner should be directed to examiner Cheng-Yuan Tseng whose telephone number is (571)272-9772, and fax number is (571)273-9772. The examiner can normally be reached on Monday through Friday from 09:00 to 17:30 Eastern Time. If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Alicia Harrington can be reached on (571)272-2330. The fax phone number for the organization where this application or proceeding is assigned is (571)273-8300. Information regarding the status of an application may be obtained from the Patent Application Information Retrieval (PAIR) system. Status information for published applications may be obtained from either Private PAIR or Public PAIR. Status information for unpublished applications is available through Private PAIR only. Should you have questions on access to the Private PAIR system, contact the Electronic Business Center (EBC) at (866)217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative or access to the automated information system, call (800)786-9199 (IN USA OR CANADA) or (571)272-1000. /CHENG YUAN TSENG/Primary Examiner, Art Unit 2615
Read full office action

Prosecution Timeline

Jan 09, 2023
Application Filed
Jan 28, 2024
Non-Final Rejection — §103
May 01, 2024
Response Filed
May 19, 2024
Final Rejection — §103
Jul 23, 2024
Response after Non-Final Action
Aug 06, 2024
Response after Non-Final Action
Aug 06, 2024
Applicant Interview (Telephonic)
Aug 23, 2024
Request for Continued Examination
Aug 27, 2024
Response after Non-Final Action
Aug 27, 2024
Response after Non-Final Action
Mar 06, 2025
Non-Final Rejection — §103
Jun 11, 2025
Response Filed
Jun 29, 2025
Final Rejection — §103
Sep 02, 2025
Response after Non-Final Action
Oct 02, 2025
Request for Continued Examination
Oct 10, 2025
Response after Non-Final Action
Oct 10, 2025
Response after Non-Final Action
Feb 05, 2026
Non-Final Rejection — §103 (current)

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Prosecution Projections

5-6
Expected OA Rounds
84%
Grant Probability
99%
With Interview (+15.7%)
2y 6m
Median Time to Grant
High
PTA Risk
Based on 835 resolved cases by this examiner. Grant probability derived from career allow rate.

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