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 .
Rejection under 35 U.S.C. §112(b)
Applicant’s arguments, see page 5, line 7 through line 10, and the cancellation of claim 9, filed6 April 2026, with respect to the rejection of claim 9 under 35 U.S.C. §112(b), have been fully considered and are persuasive. The rejection of claim 9 under 35 U.S.C. §112(b) is rendered moot and has been withdrawn.
Rejection under 35 U.S.C. §102
Applicant’s arguments, see page 5, line 11 through page 6, line 3, and the amendment to independent claims 1 and 15, filed 6 April 2026, with respect to the rejection of claims 1-8 and 12-15 under 35 U.S.C. §102(a)(1) as being anticipated by Karg et al. (U.S. Patent Application Publication No. US 2022/0004876 A1) (hereafter referred to as “Karg (‘876)”), have been fully considered and are persuasive. The rejection of claims 1-8 and 12-15 under 35 U.S.C. §102(a)(1) as being anticipated by Karg et al. (U.S. Patent Application Publication No. US 2022/0004876 A1) has been withdrawn.
Rejection under 35 U.S.C. §103
Applicant’s arguments, see page 6, line 4 through page 7, line 23, and the amendment to claims to independent claims 1 and 15, filed 6 April 2026, with respect to the rejection of claim 9 under 35 U.S.C. §103(a) as being unpatentable over Karg et al. (U.S. Patent Application Publication No. US 2022/0004876 A1) in view of Lin et al. (U.S. Patent Application Publication No. US 2016/0328647 A1), have been fully considered and are persuasive. The rejection of claim 9 under 35 U.S.C. §103(a) as being unpatentable over Karg et al. (U.S. Patent Application Publication No. US 2022/0004876 A1) in view of Lin et al. (U.S. Patent Application Publication No. US 2016/0328647 A1) has been withdrawn.
New Grounds of Rejection
The new ground of rejection using a new combination of references addresses the newly amended claimed limitations. Rejections based on the new combination of references follow.
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 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(a) 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 set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied 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 1-7 and 10-15 are rejected under 35 U.S.C. §103(a) as being unpatentable over Karg et al. (U.S. Patent Application Publication No. US 2022/0004876 A1) (hereafter referred to as “Karg (‘876)”) in view of Nikonov et al. (U.S. Patent Application Publication No. US 2020/0074268 A1) (hereafter referred to as “Nikonov”).
With regard to claim 1, Karg (‘876) describes a training unit adapted to train an oscillatory neural network, the training unit being a part of a processor (see Figure 1 and refer for example to paragraph [0026]); an oscillatory neural network unit, the oscillatory neural network unit implementing a trained oscillatory neural network being adapted to output a pattern when an image is inputted, the oscillatory neural network unit being a part of a programmable architecture (see Figures 3 and 7, and refer for example to paragraphs [0030] and [0047]); and a controlling unit adapted to control the oscillatory neural network unit and the training unit, the controlling unit being another part of the programmable architecture, the processor and the programmable architecture forming a system-on-chip, wherein the programmable architecture is a field-programmable gate array (see Figures 1 and 5 and refer for example to paragraphs [0025], [0026], [0027], [0037] and [0090]).
Although Karg does not explicitly describe that the processor is an Advanced Reduced Instruction Set Computing Machines processor, such an element is well known and widely utilized in the prior art.
Nikonov discloses an oscillatory neural network system (refer for example to the abstract) which provides for oscillatory neural network pattern when an image is inputted, the oscillatory neural network having a controlling unit and being a part of a programmable architecture, where the programmable architecture is a field-programmable gate array, and the processor and the programmable architecture forming a system-on-chip (see Figures 1, 2 and 5, and refer for example to paragraphs [0035] through [0037], [0040] through [0042], [0045] and [0051], Nikonov’s “reduced instruction set computer (RISC) processor” corresponds to applicant’s Advanced Reduced Instruction Set Computing Machines processor - see applicant’s page 10 of the specification).
Given the teachings of the two references and the same environment of operation, namely that of systems for processing an image using an oscillatory neural network, it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the Karg (‘876) system to provide for an Advanced Reduced Instruction Set Computing Machines processor in the manner described by Nikonov according to known methods to yield predictable results and would have been motivated to do so with a reasonable expectation of success in order to provide for increased processing efficiency and higher accuracy as suggested by Nikonov (refer for example to paragraph [0012]), which fails to patentably distinguish over the prior art absent some novel and unexpected result.
As to claim 2, Karg (‘876) describes wherein the oscillatory neural network unit comprises neuron blocks, each neuron block implementing a respective neuron of the oscillatory neural network (see Figures 1 and 2, and refer for example to paragraphs [0026] and [0035]); a synapse block, the synapse block being a set of memories and interconnection circuits, each memory storing a respective weight and the interconnection circuits being connected with neurons blocks (see Figure 2, element 9 and refer for example to paragraph [0036]); and the control block controlling the synapse block and the neuron blocks to implement the trained oscillatory neural network (see Figure 1, element 4 and refer for example to paragraph [0037]).
In regard to claim 3, Karg (‘876) describes wherein each neuron block comprises a phase calculator and a phase controlled oscillator (see Figure 1, element 10 and refer for example to paragraph [0029]).
With regard to claim 4, Karg (‘876) describes wherein the control block is further adapted to determine the state of the oscillatory neural network in presence of the inputted image, the state being chosen among a failure to converge, an incorrect recognition and a correct recognition (refer for example to paragraphs [0030] through [0033] and paragraph [0041]).
As to claim 5, Karg (‘876) describes wherein the controlling unit is adapted to initialize the oscillatory neural network and to synchronize the oscillatory neural network with the other elements of the pattern learning and recognition device in cooperation with the control block (refer for example to paragraph [0041]).
In regard to claim 6, Karg (‘876) describes wherein the oscillatory neural network is a fully connected neural network (as illustrated in Figures 1 and 2).
With regard to claim 7, Karg (‘876) describes wherein the training unit is adapted to implement a Hebbian learning rule or a Storkey learning rule (refer for example to paragraph [0043]).
With regard to claims 10 and 11, although Karg (‘876) discloses a computer processing device (refer for example to paragraph [0020]), Karg (‘876) does not expressly describe a display to display an information relative to the output of the oscillatory neural network unit in presence of the image, such a technique is well known and widely utilized in the prior art.
Nikonov discloses an oscillatory neural network system (refer for example to the abstract) which provides for a display to display an information relative to the output of the oscillatory neural network unit in presence of the image (see Figure 5 and refer for example to paragraphs [0041] and [0047]).
Given the teachings of the two references and the same environment of operation, namely that of systems for processing an image using an oscillatory neural network, it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the Karg (‘876) system to provide for a display to display an information relative to the output of the oscillatory neural network unit in presence of the image in the manner described by Nikonov according to known methods to yield predictable results and would have been motivated to do so with a reasonable expectation of success in order to provide for increased processing efficiency and higher accuracy as suggested by Nikonov (refer for example to paragraph [0012]), which fails to patentably distinguish over the prior art absent some novel and unexpected result.
In regard to claim 12, Karg (‘876) describes wherein the training unit is adapted to learn the weights of the oscillatory neural network to obtain learnt weights, the pattern learning and recognition device comprising a memory unit, the memory unit being adapted to store the weight learnt and being another part of the programmable architecture (refer for example to paragraphs [0029] and [0031]).
With regard to claim 13, Karg (‘876) describes wherein the training unit is adapted to train the oscillatory neural network based on images received by the image receiver (refer for example to paragraph [0030]).
As to claim 14, Karg (‘876) describes wherein the training unit is adapted to train the oscillatory neural network based on the output of the oscillatory neural network unit (refer for example to paragraphs [0031] and [0033]).
In regard to claim 15, Karg (‘876) describes a training unit, the training unit being a part of a processor (see Figure 1 and refer for example to paragraph [0026]); an oscillatory neural network unit, the oscillatory neural network unit being a part of a programmable architecture, notably a field-programmable gate array (see Figures 3 and 7, and refer for example to paragraphs [0021], [0030] and [0047]); a controlling unit, the controlling unit being another part of the programmable architecture (see Figures 1 and 5 and refer for example to paragraphs [0025], [0026], [0027] and [0037]); the processor and the programmable architecture forming a system-on-chip (refer for example to paragraphs [0023] and [0037]); the method comprising training an oscillatory neural network, implementing a trained oscillatory neural network, outputting a pattern when an image is inputted, and controlling the oscillatory neural network unit and the training unit (refer for example to paragraphs [0030], [0033], [0032], [0036] and [0037]) , wherein the programmable architecture is a field-programmable gate array (see Figures 1 and 5 and refer for example to paragraphs [0025], [0026], [0027], [0037] and [0090]).
Although Karg does not explicitly describe that the processor is an Advanced Reduced Instruction Set Computing Machines processor, such an element is well known and widely utilized in the prior art.
Nikonov discloses an oscillatory neural network system (refer to the abstract) which provides for oscillatory neural network pattern when an image is inputted, the oscillatory neural network having a controlling unit and being a part of a programmable architecture, where the programmable architecture is a field-programmable gate array, and the processor and the programmable architecture forming a system-on-chip (see Figures 1, 2 and 5, and refer for example to paragraphs [0035] through [0037], [0040] through [0042], [0045] and [0051], Nikonov’s “reduced instruction set computer (RISC) processor” corresponds to applicant’s Advanced Reduced Instruction Set Computing Machines processor - see applicant’s page 10 of the specification).
Given the teachings of the two references and the same environment of operation, namely that of systems for processing an image using an oscillatory neural network, it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the Karg (‘876) system to provide for an Advanced Reduced Instruction Set Computing Machines processor in the manner described by Nikonov according to known methods to yield predictable results and would have been motivated to do so with a reasonable expectation of success in order to provide for increased processing efficiency and higher accuracy as suggested by Nikonov (refer for example to paragraph [0012]), which fails to patentably distinguish over the prior art absent some novel and unexpected result.
Relevant Prior Art
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Velichko, Zhang, and Abernot all disclose oscillatory neural network systems used in imaging applications which are similar to applicant’s claimed invention.
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 extension fee 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 date of this final action.
Contact Information
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Jose L. Couso whose telephone number is (571) 272-7388. The examiner can normally be reached on Monday through Friday from 5:30am to 1:30pm.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Matthew Bella, can be reached on 571-272-7778. The fax phone number for the organization where this application or proceeding is assigned is (571) 273-8300.
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/JOSE L COUSO/Primary Examiner, Art Unit 2667
December 5, 2025
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