DETAILED ACTION
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 .
DETAILED ACTION
An examination of this application reveals that applicant is unfamiliar with patent prosecution procedure. While an applicant may prosecute the application (except that a juristic entity must be represented by a patent practitioner, 37 CFR 1.31), lack of skill in this field usually acts as a liability in affording the maximum protection for the invention disclosed. Applicant is advised to secure the services of a registered patent attorney or agent to prosecute the application, since the value of a patent is largely dependent upon skilled preparation and prosecution. The Office cannot aid in selecting an attorney or agent.
A listing of registered patent attorneys and agents is available at https://oedci.uspto.gov/OEDCI/. Applicants may also obtain a list of registered patent attorneys and agents located in their area by writing to the Mail Stop OED, Director of the U.S. Patent and Trademark Office, P.O. Box 1450, Alexandria, VA 22313-1450.
Status of Claims
This action is in reply to the amendments filed on September 27, 2022.
Claims 21-40 are currently pending.
Claims 21-28, 31-33, 37, and 38 have been amended.
Information Disclosure Statement
The listing of references in the specification (beginning on page 55) is not a proper information disclosure statement. 37 CFR 1.98(b) requires a list of all patents, publications, or other information submitted for consideration by the Office, and MPEP § 609.04(a) states, "the list may not be incorporated into the specification but must be submitted in a separate paper." Therefore, unless the references have been cited by the examiner on form PTO-892, they have not been considered.
Claim Objections
The previous objection to claim 33 is withdrawn in view of Applicant’s amendment.
The previous objection to claim 37 is withdrawn in view of Applicant’s amendment.
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
The previous rejection to claim 21, under 35 U.S.C. 112(a), regarding the “each neuron in the Y area is associated with one of a plurality of binary-represented connection types,” is withdrawn due to the cancellation of this language.
The previous rejection to claim 22, which was rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement, for not disclosing “the scope for neuronal competition,” is withdrawn due to the cancellation of this language.
The previous rejection to claim 23, which was rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement, for not disclosing “a positive neuron,” is withdrawn due to the cancellation of this language in claim 23.
The previous rejection to claim 26, which was rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement, for not disclosing “a receptive pre-action potential,” is withdrawn due to the cancellation of this language in claim 26.
The previous rejection to claim 27, which was rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement, for not disclosing “where k is a number that is either static or determined dynastically” and “the goodness of the match,” is withdrawn due to the cancellation of this language in claim 27.
The previous rejection to claim 28, which was rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement, for not disclosing “the post-synaptic neuron is updated by the co-firing of the two said neurons,” is withdrawn due to the cancellation of this language in claim 28.
Additionally, claim 31 is rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention. Claim 31 discloses: “updating the neural network recursively from each indexed time to next indexed time t+1” and “an unbounded number of consecutive life times” but the specification does not discuss discrete or consecutive life times nor does the specification detail “inception time 0” or “time 0.” Additionally, claim 31 discusses: “the recursive decomposition is conditioned on an incremental learning restriction, a training or exploration experience, and a limited computational resource.” The specification makes no mention of a recursive decomposition and, therefore, the specification lacks the details necessary to reasonably convey to one skilled in the art that the inventor had possession of the claimed invention at the time of filing. Applicant’s response to arguments on page 2 note that the previous rejection has been fixed by removing “an initial time” and only using the terms explicitly appearing in the specifications. However, Applicant’s argument do not explicitly disclose where these terms are in the specification. And, for example, the specification does not refer to any recursive decomposition and it is unclear how “incremental learning experience”, “sensory experience”, and “limited resource” correspond to this term and the others mentioned above.
Claim 24 is rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention. Claim 24 discloses: “any particular positive neuron” but the specification does not discuss positive neurons. Therefore, the specification lacks the details necessary to reasonably convey to one skilled in the art that the inventor had possession of the claimed invention at the time of filing.
Additionally, claim 25 is rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention. Claim 25 discloses: “wherein each post-synaptic neuron uses the strengths of connections from all the input neurons to its negative neuron to identify the boundary of its inhibition zone” but the specification does not discuss the strengths of connections from all the input neurons to its negative neuron. Therefore, the specification lacks the details necessary to reasonably convey to one skilled in the art that the inventor had possession of the claimed invention at the time of filing.
Additionally, claim 37 is rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention. Claim 37 discloses: “at least one overt and at least one covert neuron” but the specification does not discuss overt or covert neurons. Claim 37 also discloses “a recalled plan” but the specification makes no mention of a recalled plan. Therefore, the specification lacks the details necessary to reasonably convey to one skilled in the art that the inventor had possession of the claimed invention at the time of filing.
Additionally, claim 40 is rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention. Claim 40 discloses: “strong Artificial Intelligence (AI)” but the specification does not discuss strong AI. Therefore, the specification lacks the details necessary to reasonably convey to one skilled in the art that the inventor had possession of the claimed invention at the time of filing.
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.
The previous rejection of claim 22 for lacking antecedent basis for the limitation "the scope for neuronal competition" is withdrawn in view of Applicant’s amendment.
The previous rejection of claim 26 for lacking antecedent basis for the limitation "the pre-action potentials" and “competition zone” is withdrawn in view of Applicant’s amendment.
The previous rejection of claim 28 for lacking antecedent basis for the limitation " the co-firing of the two said neurons” is withdrawn in view of Applicant’s amendment.
The previous rejection of claim 31 for lacking antecedent basis for the limitations “the computation of an unbounded number of consecutive life times”, “the previous neural network machine”, “the optimal neural network”, “the recursive decomposition”, “discrete life times” and “consecutive life times” are withdrawn in view of Applicant’s amendment.
The previous rejection of claim 32 for lacking antecedent basis for the limitation "The process of Claim 21" is withdrawn in view of Applicant’s amendment.
The previous rejection of claim 37 for lacking antecedent basis for the limitation "the explicitly taught overt and covert neurons" is withdrawn in view of Applicant’s amendment.
Claim 27 recites the limitations “the top-k values” in line 3. There is insufficient antecedent basis for these limitations in the claim.
Claim 38 recites the limitations “the network” in line 2. There is insufficient antecedent basis for these limitations in the claim.
Regarding claim 24, the claim discloses the phrase “that inhibit the neuron” is unclear because it is unclear which of the plurality of neurons is “the neuron.”
Regarding claim 31, the claim discloses “a process for updating the neural network.” But, claim 21, from which claim 31 depends, is directed to an apparatus and not a process. Claim 31 does not provide any details on how the claimed process is implemented on the apparatus of claim 21 and, therefore, the scope of claim 31 is unclear.
Regarding claim 38, the claim discloses “the neural network chooses to use one plan from the one or more plans after the network has been trained with one or more plans.” Claim 37, from which claim 38 depends, recites: “Wherein the neural network is trained with one or more plans.” It appears that the claimed neural network is untrained which makes it unclear how an untrained neural network can choose a plan in which to train itself. It is not clear how a neural network, that is trained on a plan, is first able to select a plan on which to be trained. An untrained neural network cannot choose a plan and the neural network in claim 38 appears to be untrained until it selects a plan.
The previous rejection of claim 33 due to the phrase "may" rendering the claim indefinite is withdrawn in view of Applicant’s amendment.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claim(s) 21-24, 28, 30, 32, and 34-40 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Zheng et al., “Mobile Device Based Outdoor Navigation With On-line Learning Neural Network: a Comparison with Convolutional Neural Network” (Zheng).
With respect to independent claim 21 Zhen teaches:
An improvement to a developmental network implemented in computer hardware (Zheng teaches an on-line learning neural network for real-time outdoor navigation using only the computational resources available on a standard android mobile device; see abstract.), the developmental network comprising at least one neural network having a plurality of neurons organized into a hierarchy of levels comprising an X area associated with sensory information (Zheng teaches a developmental network (DN) comprising a sensory area denoted as X; see section 3.1.), a Z area associated with motor information (Zheng teaches a developmental network (DN) comprising a motor area denoted as Z; see section 3.1.), and a hidden Y area between the X area and the Z area (Zheng teaches a developmental network (DN) comprising a hidden area denoted as Y, where the Y area is constantly updated from inputs from the X and Z areas; see section 3.1.), the improvement comprising:
each neuron has an associated location in a computer simulated physical space (Zheng teaches that top-k competition is used as a simulation of global dynamic inhibition among neurons in section 3.1 and that the DN is capable of simulating the behavior of a Finite Automaton in section 5. Zheng further teaches that hidden area neurons are located on a two-dimensional grid in section 3.1.); and
the internal representation of the hierarchy of the X area, Y area, and Z area is a fluid hierarchy (Zheng teaches a developmental network (DN) comprising a hidden area, Y, that is constantly updated (fluid) from inputs from the X and Z areas; see section 3.1. Zheng also teaches updating, including Hebbian learning, that takes place in firing neurons in section 3.1.).
With respect to claim 22, the rejection of claim 21 is incorporated. Further Zheng teaches:
each neuron has an inhibition zone that is a zone for neuronal competition that determines whether the neuron fires (Zheng teaches inhibition and competition of neurons that are competing to fire in section 3.1.).
With respect to claim 23, the rejection of claim 22 is incorporated. Further Zheng teaches:
each neuron is associated with at least one negative neuron (Zheng teaches Hebbian learning in section 3.1. Page 17, section H, of the specification discloses that negative neurons are present in Hebbian learning of inhibition. Therefore, Zheng’s teaching of Hebbian learning is sufficient to teach the limitation.).
With respect to claim 24, the rejection of claim 23 is incorporated. Further Zheng teaches:
each neuron includes one or more of excitatory and inhibitory connections (Zheng teaches inhibition and competition in section 3.1. The claim states each neuron includes one or more of excitatory and inhibitory connections, which, interpreted in the alternative, requires the prior art to teach only one of excitatory or inhibitory connections.), the excitatory connections correspond to a receptive field of the neuron and the inhibitory connections correspond to a receptive field of one or more negative neurons that inhibit the neuron (Zheng teaches Hebbian learning in section 3.1. Page 17, section H, of the specification discloses that negative neurons are present in Hebbian learning of inhibition. Therefore, Zheng’s teaching of Hebbian learning is sufficient to teach the limitation.), such that the inhibition zone for any particular positive neuron changes according to a training or exploration experience of the particular neuron.
With respect to claim 28, the rejection of claim 22 is incorporated. Further Zheng teaches:
each neuron in the Y area has a neuronal weight associated with a pre-synaptic neuron and a post-synaptic neuron and wherein the post-synaptic neuron is updated by a Hebbian-like mechanism (Zheng teaches a developmental network (DN) comprising a hidden area denoted as Y and neuron weighting in section 3.1. Zheng further teaches that a winning neuron is remembered as an incremental average to the existing weight vector and a winning neuron has its bottom-up weight and top-down weight updated; see section 3.1. Further, Zheng teaches Hebbian learning in section 3.1.).
With respect to claim 31, the claim is plagued by numerous 112(a) and 112(b) issues, as indicated above. Given the number of issues, and lack of corresponding detail in the specification, it is impossible to obtain a reasonable interpretation of claim 31. Therefore, no art rejection is presented at this time.
With respect to claim 32, the rejection of claim 21 is incorporated. Further Zhen teaches:
a process for incrementally generating neurons through an experience of training or exploration (Zheng teaches a developmental network (DN) that uses Hebbian learning to achieve stable performance at early stages of training; see section 2, page 12 right column.).
With respect to claim 34, the rejection of claim 21 is incorporated. Further Zheng teaches:
a learning process, wherein the neural network learns an emergent Turing machine or an emergent universal Turing machine as representations of experience of teaching or exploration (Zheng teaches the developmental network (DN) in section 3.1. The instant specification discloses DN’s may be Turing machines in at least section V on page 27. Additionally, the specification seems to indicate that Turing machines are known in the art: “Turing machine [31], [11], [19] is a better computation model in this sense as it offers an additional read-write head that allows the agent to alter the input tape” on page 27 of the instant spec.).
With respect to claim 35, the rejection of claim 21 is incorporated. Further Zheng teaches:
the fluid hierarchy internally spans a lifetime (Zheng teaches the DN is constantly updated in section 3.1. The claim and specification provide no guidance on the internal lifetime and constant updating during the duration of the DN is considered a lifetime.).
With respect to claim 36, the rejection of claim 21 is incorporated. Further Zheng teaches:
the neural network selectively uses or selectively disregards combinations of features at different levels of representations within the fluid hierarchy (Zheng teaches concatenating feature vectors at various stages of a convolutional neural network in figure 4 on page 15. Concatenating features is selectively using features and figure 4 teaches different levels.).
With respect to claim 37, the rejection of claim 21 is incorporated. Further Zheng teaches:
the Z area of the neural network associated with motor information has at least one overt and at least one covert neuron for each of a plurality of effectors (Zheng teaches a developmental network (DN) comprising a motor area denoted as Z; see section 3.1. The specification does not teach overt or covert neurons. At best, the specification teaches planning with overt and covert actions. Zheng teaches );
wherein the neural network is trained with one or more plans with or without task costs using a multiplicity of explicitly taught overt and covert neurons (Zheng teaches a developmental network (DN) that is constantly updated from inputs from the X and Z areas; see section 3.1. The abstract further discusses training. Additionally, the claimed plans are not detailed and the methods used to train any neural network can be considered a plan. Further, the limitation “with or without costs” is not limiting as it covers all possible scenarios. Networks can be trained considering costs or not considering costs and a network that has some sort of partial cost consideration is still considering costs.); and
wherein a recalled experienced skill that is not exactly the same as taught or explored is called an emergent skill (This limitation is given no patentable weight for being nonfunctional descriptive material; see MPEP 2111.05. It merely assigns a descriptor (emergent skill) to a situation but does not implement or use the emergent plan in any fashion.).
With respect to claim 38, the rejection of claim 37 is incorporated. Further Zheng teaches:
the neural network chooses to use one plan from the one or more plans after the network has been trained with one or more plans (Zheng teaches a developmental network (DN) that is constantly updated from inputs from the X and Z areas; see section 3.1. The abstract further discusses training. Additionally, the claimed plans are not detailed and the methods used to train any neural network can be considered a plan. Further, there are no details regarding how the network chooses the plan and it is unclear how an untrained network can make a selection.).
With respect to claim 39, the rejection of claim 21 is incorporated. Further Zheng teaches:
a use of the neural network for at least two modalities of vision (Zheng teaches using a standard android device including a camera and GPS; see abstract.), audition (Figure 2 of Zheng discloses audio instructions (audition).), natural language, in a real or a simulated environment (Figure 2 of Zheng discloses implementation in a real environment.).
With respect to claim 40, the rejection of claim 21 is incorporated. Further Zheng teaches:
a use of the neural network for strong Artificial Intelligence (AI) wherein the strong AI is not task-specific but is conditioned on incremental learning framework restrictions, a learning or exploration experience, and a limited amount of computational resources (Zheng teaches a developmental network (DN) capable of performing a variety of tasks; see section 3.1. and Table 3. The claims do not specify the scope of the tasks nor does the claim detail the strong AI. The variety of tasks described in Table 3 and the DN taught by Zhen are sufficient to teach the broad limitations.).
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.
The factual inquiries 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.
Claims 25-27, 29, and 33 is/are rejected under 35 U.S.C. 103 as being unpatentable over Zheng et al., “Mobile Device Based Outdoor Navigation With On-line Learning Neural Network: a Comparison with Convolutional Neural Network” (Zheng); in view of Luciw et al., “Top-Down Connections in Self-Organizing Hebbian Networks: Topographic Class Grouping” (Luciw).
With respect to claim 25, the rejection of claim 24 is incorporated. Further Zheng does not explicitly teach:
each neuron is classified as presynaptic, post-synaptic, or both; and
wherein each post-synaptic neuron uses the strengths of connections from all the input neurons to its negative neuron to identify the boundary of its competition zone.
However, Luciw teaches these limitations:
each neuron is classified as presynaptic, post-synaptic, or both (Luciw teaches updating neurons through postsynaptic and/or presynaptic activity; see left column on page 252.); and
wherein each post-synaptic neuron uses the strengths of connections from all the input neurons to its negative neuron to identify the boundary of its competition zone (Luciw also teaches updating the weight (strengths) of neurons in the left column on page 252.).
Zheng and Luciw are analogous art directed towards neural networks. Zheng teaches implementation of a Developmental Network on mobile hardware and Luciw teaches a top-down Hebbian network including various arrangement and display details.
It would have been obvious for one of ordinary skill in the art of fault analysis to incorporate Luciw’s neuron aspects, arrangement, and display details into Zheng’s system before the effective filing date of the claimed invention. It would have been obvious because on of ordinary skill would be motivated to implement a system of incremental learning that allows for stable feature extraction in visual recognition; see abstract.
With respect to claim 26, the rejection of claim 25 is incorporated. Zheng teaches:
each post-synaptic neuron determines whether to fire by comparing its pre-action potential with all the pre-action potentials of all neurons in its competition zone (Zheng teaches neurons with the highest firing values inhibit firing in the other irrelevant neurons on page 12. Determining neurons with a highest firing value implies a comparison.).
Further Luciw teaches:
pre-synaptic and post-synaptic neurons; see left column on page 252
each neuron has a pre-response (Luciw teaches precomputation that includes computing a competitive potential; see section IV. on page 251.) and
See the rejection of claim 25 for the motivation to combine references.
With respect to claim 27, the rejection of claim 25 is incorporated. Further Zheng teaches:
each post-synaptic neuron has a pre-response and wherein each post-synaptic neuron makes the determination of whether to fire by determining the goodness of the match between its weights and its input pattern is valued among the top-k values in the inhibition zone (Zheng teaches inhibition and competition including using top-k to simulate inhibition among neurons; see section 3.1.), where k is a number that is either static or determined dynastically (Zhen teaches that top-k analyzes the k neurons with the highest pre-response value in section 3.1. Dynastically is interpreted as power/wealth etc. and the highest k neurons is considered dynastic.).
Further Luciw teaches:
pre-response, pre-synaptic and post-synaptic neurons; see left column on page 252 and Luciw’s teaching of precomputation that includes computing a competitive potential; see section IV. on page 251.
See the rejection of claim 25 for the motivation to combine references.
With respect to claim 29, the rejection of claim 21 is incorporated. Further Luciw teaches:
each associated location is a 3D location and the computer-simulated physical space is a 3D space (Luciw teaches a top-down self-organizing Hebbian network arranged in a 3D configuration; see abstract and figure 1.).
See the rejection of claim 25 for the motivation to combine references.
With respect to claim 30, the rejection of claim 29 is incorporated. Further Luciw teaches:
a process for computer visualization of the neurons on a computer screen that shows one or more neuronal parameters for each neuron according to its associated location (Luciw teaches various visualization methods in figure 7 on page 255.).
See the rejection of claim 25 for the motivation to combine references.
With respect to claim 33, the rejection of claim 21 is incorporated. Further Zheng teaches:
an initialization process for constructing the neural network that specifies whether to connect one or more of the X, Y, and Z areas (Zhen teaches the connection between the hidden area (Y area) of the DN and motor area is bidirectional (binary-represented) in section 3.1. Zheng also teaches a developmental network (DN) that uses Hebbian learning to achieve stable performance at early stages of training; see section 2, page 12 right column.); and
a synaptic maintenance process for changing the fluid hierarchy (Zheng teaches a developmental network (DN) that is constantly updated (fluid) from inputs from the X and Z areas; see section 3.1.);
and during the synaptic maintenance, each post-synaptic neuron determines whether to cut an input connection where a deviation of weights is high and re-connect an input connection when the deviation of weights is low (Zheng teaches an on-line learning neural network for real-time outdoor navigation using only the computational resources available on a standard android mobile device; see abstract.).
Luciw teaches:
wherein each neuron is classified as pre-synaptic, post-synaptic, or both (Luciw teaches pre-synaptic and post-synaptic neurons; see left column on page 252.)
See the rejection of claim 25 for the motivation to combine references.
Response to Arguments
Applicant's arguments filed June 1, 2022 have been fully considered but they are not persuasive.
Applicant should submit an argument under the heading “Remarks” pointing out disagreements with the examiner’s contentions. Applicant must also discuss the references applied against the claims, explaining how the claims avoid the references or distinguish from them. Examiner has indicated in the rejection above which prior rejections have been corrected, which prior rejections have been maintained, and why, and has also introduced new rejections introduced in the amendments. Applicant’s arguments are not persuasive.
Conclusion
Claims 21-40 are rejected.
Examiner strongly suggests Applicant reach out (contact information below) to schedule an interview to discuss any aspects of the rejection that are unclear.
THIS ACTION IS MADE FINAL. 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 mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to DANIEL T PELLETT whose telephone number is (571)270-7156. The examiner can normally be reached Monday - Friday 9-5 EST.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Li Zhen can be reached on 571-272-3768. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/DANIEL T PELLETT/Primary Examiner, Art Unit 2121