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 office action is in response to the applicant’s arguments filed on August 21, 2026, in which claims 1-20 are presented for further examination.
Response to Arguments
Applicants’ arguments with respect to claims 1-20 have been considered but are moot because the new ground of rejection necessitated by the Information Disclosure Statement (IDS).
The 35 USC 101 rejection set forth in the last office action has been withdrawn in lieu of the Applicant’s arguments.
A new ground of rejection is hereby provided in lieu of the IDS filed on August 03, 2026.
Information Disclosure Statement
The information disclosure statement filed on August 03, 2026, complies with the provisions of 37 CFR 1.97, 1.98 and MPEP § 609. It has been placed in the application file. The information referred to therein has been considered as to the merits.
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 Li Xiaqing et al., (hereinafter “Xiaqing”) article entitled “Deep Learning and Its Parallelization: Concepts and Instances”, 1 January 2015 (2015-01-01), pages 1-29, XP093241327, Retrieved from the Internet: JRLthttp://www.cs.newpaltz.edu/-lik/publications/ Xiaqing-Li-BDPP-2016.pdf” in view of Griffiths et al., (hereinafter Griffiths”) article entitled "Scalable visible light communications with a micro-LED array projector and high-speed smartphone camera", Optics Express, vol. 27, no. 11, 17 May 2019 (2019-05-17), page 15585, XP093241523, US, ISSN: 1094-4087, DOI: 10.1364/OE.27.015585”.
As to claim 1, Xiaqing discloses a method for processing an artificial intelligence (AI) model (abstract; Section 2.1.1; Section 3.3.2; fig. 2 and 18), the method comprising:
a first set of neurons associated with a first layer of the Al model communicating via electromagnetic radiation: (1) reference signals for facilitating a collective operation associated with the Al model, and (2) input signals for use with a second layer of the AI model for performing the collective operation associated with the AI model (Section 2.1.1; fig. 2 disclose the general processing of a neural network where neurons of a first layer communicate with networks of a second layer by providing the input signals for performing an operation, e.g. training or inference. Section 3.3.2; fig. 18 disclose an implementation on various devices of communications between the neurons of different layers assigned to the devices); and
a second set of neurons associated with the second layer receiving,
However, Xiaqing does not explicitly disclose the claimed “[[the first set of neurons communicate]] via electromagnetic radiation: reference signals for facilitating a collective operation associated with the AI model; and [[the second set of neurons receive]] as a result of processing of a property of the electromagnetic radiation, the reference signals”.
On the other hand, Griffiths discloses the claimed “[[the first set of neurons communicate]] via electromagnetic radiation: reference signals for facilitating a collective operation associated with the AI model; and [[the second set of neurons receive]] as a result of processing of a property of the electromagnetic radiation, the reference signals” (abstract; Section 2, fig. 2 "Spatial patterns are projected onto a wall at a refresh rate of 480 Hz, which can be captured by the smartphone's 960 fps camera" disclose the features of communicating via electromagnetic radiation reference signals, in particular projecting the electromagnetic radiation onto a wall, Section 1 "In order to push to high data rates additional dimensions, such as colour, intensity and cell shape can be exploited within the transmitted image" disclose using multiple properties of the electromagnetic radiation to transmit information).
Therefore, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Xiaqing to use an electromagnetic radiation: reference signals for facilitating a collective operation associated with the AI model as a result of processing of a property of the electromagnetic radiation, the reference signals, in order to transmit reference signals for facilitating a collective operation associated with the AI model.
As to claim 9, Xiaqing discloses a method for processing an artificial intelligence (AI) model comprising L. layers, wherein L is an integer greater than or equal to 2 (abstract; Section 3.3.2; fig. 18)
Xiaqing does not disclose the following features: “using associated projectors, a first set of the processing units: projecting electromagnetic radiation on a surface corresponding to reference signals for facilitating a collective operation associated with two of the L layers of the AI model, and projecting electromagnetic radiation on the surface corresponding to any population sum signals for use with the collective operation associated with the two of the L layers of the Al model; and using at least one electromagnetic radiation sensor pointed at the surface, a second set of processing units acquiring the reference signals, and any population sum signals for use with the collective operation associated with the two of the L layers of the AI model”.
On the other hand, Griffiths discloses the claimed “using associated projectors, a first set of the processing units: projecting electromagnetic radiation on a surface corresponding to reference signals for facilitating a collective operation associated with two of the L layers of the AI model, and projecting electromagnetic radiation on the surface corresponding to any population sum signals for use with the collective operation associated with the two of the L layers of the Al model; and using at least one electromagnetic radiation sensor pointed at the surface, a second set of processing units acquiring the reference signals, and any population sum signals for use with the collective operation associated with the two of the L layers of the AI model” (see abstract; Section 2, fig. 2 "Spatial patterns are projected onto a wall at a refresh rate of 480 Hz, which can be captured by the smartphone's 960 fps camera" disclose the features of communicating via electromagnetic radiation reference signals, in particular projecting the electromagnetic radiation onto a wall, and Section 1 "In order to push to high data rates additional dimensions, such as colour, intensity and cell shape can be exploited within the transmitted image" disclose using multiple properties of the electromagnetic radiation to transmit information).
Therefore, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Xiaqing to use associated projectors, a first set of the processing units: projecting electromagnetic radiation on a surface corresponding to reference signals for facilitating a collective operation associated with two of the L layers of the AI model, and projecting electromagnetic radiation on the surface corresponding to any population sum signals for use with the collective operation associated with the two of the L layers of the Al model; and use at least one electromagnetic radiation sensor pointed at the surface, a second set of processing units acquiring the reference signals, and any population sum signals for use with the collective operation associated with the two of the L layers of the AI model for facilitating a collective operation associated with the AI model as a result of processing of a property of the electromagnetic radiation, the reference signals, in order to transmit reference signals for facilitating a collective operation associated with the AI model.
As to claim 15, claim 15 is a system for performing the method of claim 1. Claim 15 is rejected under the same rationale.
As to claim 2, the combination Xiaqing and Griffiths discloses the invention as claimed. In addition, Griffiths discloses the claimed wherein the property of the electromagnetic radiation comprises luminous intensity, color, wavelength, a polarization- related property, a fluorescence-related property, a phosphorescence-related property, a storage-related property, a reflection-related property, or a combination of one or more of the aforementioned properties (see Section 1 "In order to push to high data rates additional dimensions, such as colour, intensity and cell shape can be exploited within the transmitted image").
As to claim 3, the combination Xiaqing and Griffiths discloses the invention as claimed. In addition, Xiaqing discloses the claimed wherein the reference signals comprise signals indicative of participation in the collective operation by neurons from among at least the first set of neurons or the second set of neurons (see Section 2.1.1; fig. 2 and 18).
As to claim 4, the combination Xiaqing and Griffiths discloses the invention as claimed. In addition, Xiaqing discloses the claimed wherein the first set of neurons correspond to a first set of processing units and the second set of neurons correspond to a second set of processing units, wherein the reference signals comprise individual reference signals for allowing normalization of the reference signals across at least one of the first set of processing units or the second set of processing units (see Section 3.3.2; fig. 18).
As to claim 5, the combination Xiaqing and Griffiths discloses the invention as claimed. In addition, Xiaqing discloses the claimed wherein the first set of neurons correspond to a first set of processing units and the second set of neurons correspond to a second set of processing units, and wherein the reference signals comprise population reference signals for tracking a population of at least one of the first set of processing units or the second set of processing units (see Section 3.3.2; fig. 18, and the remaining features only disclose minor implementation details which are trivial to the skilled person from his own common general knowledge).
As to claim 6, the combination Xiaqing and Griffiths discloses the invention as claimed. In addition, Xiaqing discloses the claimed wherein the input signals for use with the second layer comprise binary-encoded population sum signals (see Section 3.3 and 3.3.2; fig. 18).
As to claim 7, the combination Xiaqing and Griffiths discloses the invention as claimed. In addition, Griffiths discloses the claimed wherein the AI model comprises L layers, wherein L is an integer greater than or equal to 2, wherein the first layer corresponds to layer L-1 and the second layer corresponds to layer L, and wherein the input signals are used for inference using the AI model (see abstract; Section 2, fig. 2 "Spatial patterns are projected onto a wall at a refresh rate of 480 Hz, which can be captured by the smartphone's 960 fps camera" disclose the features of communicating via electromagnetic radiation reference signals, in particular projecting the electromagnetic radiation onto a wall, and Section 1 "In order to push to high data rates additional dimensions, such as colour, intensity and cell shape can be exploited within the transmitted image" disclose using multiple properties of the electromagnetic radiation to transmit information).
As to claim 8, the combination Xiaqing and Griffiths discloses the invention as claimed. In addition, Griffiths discloses the claimed wherein the AI model comprises L layers, wherein L is an integer greater than or equal to 2, wherein the first layer corresponds to layer L and the second layer corresponds to layer L-1, and wherein the input signals are used for training the AI model (see abstract; Section 2, fig. 2 "Spatial patterns are projected onto a wall at a refresh rate of 480 Hz, which can be captured by the smartphone's 960 fps camera" disclose the features of communicating via electromagnetic radiation reference signals, in particular projecting the electromagnetic radiation onto a wall, and Section 1 "In order to push to high data rates additional dimensions, such as colour, intensity and cell shape can be exploited within the transmitted image" disclose using multiple properties of the electromagnetic radiation to transmit information).
As to claims 10-14, the limitations of claims 10-14 have been detailed in the rejection of claims 2-8 above. They are rejected under the same rationale.
As to claims 16-20, the limitations of claims 16-20 have been detailed in the rejection of claims 2-8 above. They are rejected under the same rationale.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
US 20230078569 (involved in providing an artificial neural network comprising a compute-in-memory accelerator, the artificial neural network further comprising a hidden layer including a first plurality of artificial neurons; and training the artificial neural network using a bitwise modified rectified linear unit activation function for ones of the first plurality of artificial neurons, the bitwise modified rectified linear unit activation function comprising a bit activation function, which is configured to generate an output that is proportional to an input when the input is less than a critical threshold and configured to generate an output that is independent of the input when the input is greater than the critical threshold, wherein the input comprises a sum, across a second plurality of artificial neurons of a preceding layer of the artificial neural network having a plurality of weights associated therewith, respectively, of a product of an output from a respective one of the second plurality of artificial neurons and one bit of a respective one of the plurality of weights.)
US 20200057561 (involved in performing the access operation for the AI command locally, the storage controller utilizes various optimizations that are suited to the unique structure of AI application data (AI data).)
US 20220076120 A1 (involved in performing fine tuning of trained artificial neural network such as convolutional neural network, recurrent neural network and long term short memory (LTSM) network.)
Applicant's submission of an information disclosure statement under 37 CFR 1.97(c) with the timing fee set forth in 37 CFR 1.17(p) on August 03, 2026, prompted the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 609.04(b). 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.
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/JEAN M CORRIELUS/Primary Examiner, Art Unit 2159 October 6, 2026
