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 .
About the Invention
Claims 1-20 have been presented and pending in the application.
The examiner notes that the following references, in addition to teachings of the examiner cited references, teach the well-known systems similar to the claimed invention.
Kumar Addepalli et al. (US 2020/0250510 A1) teaches AI/ML with the well-known matrix computation
Liu et al. (US 2020/0272920 A1) teaches AI/ML systems with the well-known “load balancing”/”workload partitioning” scheduling, See paragraph 43
Maloney (US 2020/0349362 A1) teaches artificial neural network systems with a plurality of different types of sensors (including camera sensors) for processing data;
MUELLER et al. (US 2020/0348664 A1) & Zejda et al. (US 11204,747 B1), the well-known teachings relevant to claimed recitations can be found in the parent application 16/987095, see the 103 art rejection details; &
See the prior art of record that teaches the similar system of the claimed invention.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-20 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
In Claim 1;
In line 6, the phrase “generated outside of the device according to a second portion” lacks functional/operational antecedent basis. (i.e., the recited/claimed invention does not provide any support for the “according to”, since not relationship between the second portion and first data have been provided; how is the input generated functionally/structurally connected to the second portion? How does the device, having the second portion, generate the input from the outside of the device? The physical & functional connections between the second portion and the generated input are unclear and unstated from the claimed invention.).
In line12, the phrase “according to the first portion”, the above unclarities of the above discussed “according to a second portion” are similarly applied.
In line 13, the phrase “based on a combination of the sensor data and the first data” are unclear from the context of the claimed invention (i.e., it is unclear and unstated as to how the “first data” is based on the generation. Does the artificial neural network & matrix computation ever been utilized for the generation of the second output? Based on the BRI interpretation of the claimed invention, the “second output” can be generated simply by AI network).
The intended function of the “second portion” is unclear and unstated from the context of the claimed invention (i.e., what is the claimed function of the “second portion”? does the second portion functionally, operationally and/or physically participate in any way for generating first and/or second output?).
In Claim 2;
In line 2, the phrase “partition the artificial neural network into the first portion and the second portion” is not supported by the claimed invention (i.e., the recited claimed invention appears to lack support for such recited phrase).
In Claim 3;
In line 2, the phrase “dynamically distribute at least one computation to the first portion and the second portion” is not supported by the claimed invention (i.e., the recited claimed invention appears to lack support for such recited phrase).
In Claim 4;
In line 2, the phrase “at lest one instruction” lacks proper and clear antecedent basis.
In Claim 5;
In line 2, the phrase “second data representative of at least one parameter for the first portion” lacks functional antecedent basis, in addition, the claimed invention appears/fails to support the above recited phrase (e.g., who or what provides such data?).
In Claim 6;
In line 2, the phrase “third data representative of the at least one instruction to implement the at least one matrix computing of the first portion” lacks functional antecedent basis, in addition, the claimed invention appears/fails to support the above recited phrase (e.g., who or what provides such data?).
In Claim 13;
Due to the similarity between the claim 1 and 13, the unclarities of the claim 1 are similarly applied.
In Claim 19;
In line 2, it is unclear as to who or what performs “receiving sensor data from at least one sensor”.
In line 3, it is unclear as to who or what performs “partitioning an artificial neural network”.
In line 4, it is unclear as to who or what performs “implementing at least one matrix computing”.
In line 7, it is unclear as to who or what performs “generating a second output based on the sensor data”.
In lines 1-8, the claimed steps appeared to be able to performed by one or more operators using one or more computers with matrix computation program; if so, the claimed invention is appears to be well-known practices in the art, and the claimed method can appears to be mental steps can that can easily be performed by one or more operators; if not, the clear metes and bounds of the claimed invention cannot be properly determined by the examiner.
In Claim 20;
In line 1-3, the claimed recitations lack proper and clear antecedent basis.
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.
Claims 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over Rajan Kesavelu Shekar et al. (US 2020/0324073 A1), this reference will be referred to as a Rajan reference.
Examiner relies on the entire of the Rajan reference for this rejection; hence, the examiner advises the applicant to carefully consider the entire teachings of the Rjan reference to better understand the examiner’s position & the interpretations applied to the claimed invention as well as the RKS reference.
The Rajan reference teaches functionally equivalent limitations/recitations of the claimed invention, when the examiner applies Broadest Reasonable Interpretation, as follows:
CLAIMS 1, 13 & 19
Rajan REF. Teachings (emphasis underlined)
1. A device, comprising:
Fig 1/2 with accompanying description
at least one interface configured to:
Fig 1/2 with accompanying description, all inputs of 100/200
receive, from at least one sensor, sensor data as a first input to a first portion of an artificial neural network
Fig 1/2 with accompanying description; par 47, “A sensor module 210 may have a sensor input 202…may have sensor output 208”, see also par 52, “may select the profile for each machine learning model 220, 236 to use via the user selection input 204”
partitioned into the first portion and a second portion; and
Fig 1/2 with accompanying description; par 51, “one or more profiles each corresponding to a configuration of each… machine learning model 220 and …machine learning model 236”
receive first data generated outside of the device according to the second portion;
Fig 1/2 with accompanying description; par 52, “may select the profile for each machine learning model 220, 236 to use via the user selection input 204”
at least one processing unit configured to: execute at least one instruction to implement at least one matrix computation of the first portion of the artificial neural network
Fig 1/2 with accompanying description; par 51, “The term machine learning as used in present disclosure…as a neural network…trained using some training data…implemented as software running on a microprocessor, graphics processor or using a more dedicated …AI processor…neural network” (examiner notes that the claimed matrix computation is an obvious portions/parts of AI/neural network training process
to generate a first output; and
Fig 1/2 with accompanying description; par 47, “machine learning model 220 may have a second output connected to a first input 216”
generate, according to the first portion, a second output based on a combination of the sensor data and the first data.
Fig 1/2 with accompanying description; par 47, “machine learning model 220 may have a first output 214 connected to …”
13. An apparatus, comprising:
Fig 1/2/5 with accompanying description
a memory device configured to: store first data representative of at least one parameter of a first portion of an artificial neural network
Fig 1/2 with accompanying description; par 50, “sensor data received on the sensor input 202 may include context attribute data…user attribute data”; par 53-54, “user may apply an additional control data via user control input 212…machine learning model 220 may be adjusted by using the additional input as training data to further adapt the machine learning model 220…which may subsequently be stored in the memory 222”
partitioned into the first portion and a second portion; and
Fig 1/2/5 with accompanying description; par 51, “one or more profiles each corresponding to a configuration of each… machine learning model 220 and …machine learning model 236”
store second data representative of at least one instruction executable to implement at least one matrix computation of the first portion of the artificial neural network using the first data;
Fig 1/2/5 with accompanying description; par 51, “memory 222 may include one or more profiles…configuration of…machine learning model 220…236…may include additional training data…context attribute data, user attribute data…further adapt each of the machine learning model…control values…”
at least one processing unit configured to: execute, using the at least one parameter, the at least one instruction to implement at least one matrix computation of the first portion of the artificial neural network
Fig 1/2/5 with accompanying description; par 51, “The term machine learning as used in present disclosure…as a neural network…trained using some training data…implemented as software running on a microprocessor, graphics processor or using a more dedicated …AI processor…neural network” (examiner notes that the claimed matrix computation is an obvious portions/parts of AI/neural network training process
to generate a first output; and
Fig 1/2/5 with accompanying description; par 47, “machine learning model 220 may have a second output connected to a first input 216”
generate, according to the first portion, a second output based on the first data.
Fig 1/2 with accompanying description; par 47, “machine learning model 220 may have a first output 214 connected to …”
19. A method, comprising: receiving sensor data from at least one sensor ;partitioning an artificial neural network into a first portion and a second portion; implementing, to generate a first output and by utilizing at least one processing unit of a device, at least one matrix computing of the first portion of the artificial neural network based on execution of at least one instruction; and generating, but utilizing the at least one processing unit of the device, a second output based on the sensor data.
The teachings of the claim 1 are similarly applied.
As for the independent claims 1, 13 & 19, the examiner notes that the Rajan reference does not appear to expressly identify data with numbers (e.g., first, second…) for performing matrix computation; however, such not expressly disclosed data identification can easily be understood, recognized or utilized by one having ordinary skill in the art, since the Rajan reference teachings of the input and/or output data provides or teaches the functionally equivalent limitations of the claimed invention. As per claim 13, the Rajan reference openly teaches both the sources of input data from a memory, a user, and/or ML Model; therefore, the Rajan reference also teaches/performs the functionally equivalent limitations of the claimed invention; moreover, providing input/output data to/from a user or a memory are equally well-known & practiced in the art for performing the claimed functions (i.e., data stored/coming from a memory operations are considered obvious data gathering operations that are well-known and commonly practiced in the art of the claimed invention as well as the Rajan reference). Therefore, it would have been obvious to one having ordinary skill in the art before effective filing date of the recited invention to come up with the claimed method/system from the functionally equivalent teachings of the Rajan reference for the detailed teachings & reasons discussed above.
The dependent claims are also obvious from the teachings of the Rajan reference as follows:
CLAIMS 2-12, 14-18 & 20
Rajan REF. Teachings (emphasis underlined)
2. The device of claim 1, wherein the at least one processing unit is further configured to partition the artificial neural network into the first portion and the second portion based on a description of the artificial neural network.
Fig 1/2/5 with accompanying description; par 51, “one or more profiles each corresponding to a configuration of each… machine learning model 220 and …machine learning model 236”
3. The device of claim 1, wherein the at least one processing unit is further configured to dynamically distribute at least one computational task to the first portion and the second portion to a plurality of devices based on a current workload of the plurality of devices.
Obvious from the teachings of Fig 1/2/5 with accompanying description where the workload distribution in training or complex calculation is well known and commonly practiced in the for better efficiency
4. The device of claim 1, wherein the at least one processing unit is further configured to generate the at least one instruction from a sensor fusion portion of the artificial neural network.
Obvious from the teachings of Fig 1/2/5 with accompanying description; par 51, “The term machine learning as used in present disclosure…as a neural network…trained using some training data…implemented as software running on a microprocessor, graphics processor or using a more dedicated …AI processor…neural network” (examiner notes that the claimed matrix computation is an obvious portions/parts of AI/neural network training process; see also par 64-74
5. The device of claim 1, wherein the at least one interface is further configured to receive second data representative of at least one parameter for the first portion of the artificial neural network.
Obvious from the teachings Fig 1/2 with accompanying description; par 50, “sensor data received on the sensor input 202 may include context attribute data…user attribute data”; par 53-54, “user may apply an additional control data via user control input 212…machine learning model 220 may be adjusted by using the additional input as training data to further adapt the machine learning model 220…which may subsequently be stored in the memory 222”
6. The device of claim 5, wherein the at least one interface is further configured to receive third data representative of the at least one instruction to implement the at least one matrix computing of the first portion of the artificial neural network.
Obvious from the teachings Fig 1/2/5 with accompanying description; par 50, “sensor data received on the sensor input 202 may include context attribute data…user attribute data”; par 53-54, “user may apply an additional control data via user control input 212…machine learning model 220 may be adjusted by using the additional input as training data to further adapt the machine learning model 220…which may subsequently be stored in the memory 222”
7. The device of claim 1, wherein the device further comprises the at least one sensor, and wherein the at least one interface is further configured to receive the sensor data from the at least one sensor via an internal connection.
Obvious from the teachings Fig 1/2/5 with accompanying description; par 50, “sensor data received on the sensor input 202 may include context attribute data…user attribute data”; par 53-54, “user may apply an additional control data via user control input 212…machine learning model 220 may be adjusted by using the additional input as training data to further adapt the machine learning model 220…which may subsequently be stored in the memory 222”
8. The device of claim 7, wherein the at least one interface is further configured to store the sensor data from the at least one sensor in a memory device of the device.
Obvious from the teachings Fig 1/2/5 with accompanying description; par 50, “sensor data received on the sensor input 202 may include context attribute data…user attribute data”; par 53-54, “user may apply an additional control data via user control input 212…machine learning model 220 may be adjusted by using the additional input as training data to further adapt the machine learning model 220…which may subsequently be stored in the memory 222”
9. The device of claim 1, wherein the second output comprises an identification or classification of an object determined based on the combination of the first data and the sensor data.
Obvious from the teachings of Fig 1/2/5 with accompanying description; par 51, “one or more profiles each corresponding to a configuration of each… machine learning model 220 and …machine learning model 236”
10. The device of claim 1, wherein the at least one processing unit is further configured to provide at least a portion of the first output to an external device as a second input to a third portion of the artificial neural network.
Obvious from the teachings of Fig 1/2/5, where the inputs and outputs with additional devices are connected with each other
11. The device of claim 10, wherein the at least one interface is further configured to receive a result from the third portion of the artificial neural network based on the second input, wherein the result is redundant to the second output.
Obvious from the teachings of Fig 1/2/5, where the inputs and outputs with additional devices are connected with each other
12. The device of claim 1, wherein the at least one interface is further configured to communicate with a plurality of external devices to identify an external device of the plurality of external devices to generate a third output for the artificial neural network.
Obvious from the teachings of Fig 1/2/5, where the inputs and outputs with additional devices are connected with each other
14. The apparatus of claim 13, wherein the apparatus further comprises at least one sensor configured to generate sensor data to be processed by the artificial neural network.
Obvious from the teachings of Fig 1/2/5 with accompanying description; par 47, “A sensor module 210 may have a sensor input 202…may have sensor output 208”, see also par 52, “may select the profile for each machine learning model 220, 236 to use via the user selection input 204”
15. The apparatus of claim 14, wherein the apparatus further comprises at least one interface configured to: receive, from at least one sensor, the sensor data as a first input to the first portion of the artificial neural network; and
Fig 1/2 with accompanying description; par 47, “A sensor module 210 may have a sensor input 202…may have sensor output 208”, see also par 52, “may select the profile for each machine learning model 220, 236 to use via the user selection input 204”
receive, from an external device, the second data generated outside of the device according to the second portion.
Fig 1/2 with accompanying description; par 52, “may select the profile for each machine learning model 220, 236 to use via the user selection input 204”
16. The apparatus of claim 13, wherein the memory device is further configured to store an indication of a progress status of a current run of the at least one instruction.
Obvious from the teachings of Fig 1/2/5 where status indication can easily be added when desired (examiner notes that status indication or monitoring is well-known practices in the art)
17. The apparatus of claim 13, wherein the memory device is further configured to store an indication to trigger an autonomous execution of the at least one instruction.
Obvious from the teachings of Fig 1/2/5 where trigger/status indication can easily be added when desired (examiner notes that trigger/status indication or monitoring is well-known practices in the art)
18. The apparatus of claim 13, wherein the at least one processing unit is further configured to generate the second output based on the first data and sensor data.
Obvious from the teachings of Fig 1/2/5 with accompanying description; par 47, “machine learning model 220 may have a first output 214 connected to …”
20. The method of claim 19, further comprising distributing at least one computational task to the first portion and the second portion based on corresponding workloads.
The teachings of the claim 3 are similarly applied.
The dependent claims further add obvious variations of added or additional functions that are well-known & common practices in the art of data processing systems, such as the claimed system as well as the Rajan reference system; therefore, it would have been obvious to one having ordinary skill in the art before effective filing date of the recited invention to come up with the claimed method/system from the functionally equivalent teachings of the Rajan reference, with the added functions that desired by the designer/application, for the detailed teachings & reasons discussed above.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to CHRISTOPHER B SHIN whose telephone number is (571)272-4159. The examiner can normally be reached 8:00-4:00 PM.
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/CHRISTOPHER B SHIN/ Primary Examiner, Art Unit 2181
