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 action is in response to the application filed 12/21/2023 and the preliminary amendment filed 1/09/2024. In the amendment, claims 4-7, 9, 12 and 19 were amended, and no claims were cancelled or added. As such, claims 1-20 are pending and have been examined. Claims 1-20 are rejected.
Priority
The present application is a continuation application of international application no. PCT/CN2022/101128 filed 06/24/2022, which claims foreign priority to Chinese application no. CN202110704382.6 filed 06/24/2021. The examiner acknowledges that a certified copy of Chinese Application No. CN202110704382.6 (in Chinese) was retrieved 02/01/2024, as required by 37 CFR 1.55. The examiner notes that a translation of Chinese application number CN202110704382.6 does not appear to have been furnished to-date.
Although a certified copy of the foreign priority application was retrieved, a translation of said application has not yet been made of record in accordance with 37 CFR 1.55. See MPEP §§ 215 and 216. Applicant is reminded of requirements set forth in 37 CFR 1.55(g)(3)-(4) Claim for foreign priority:
“(3) An English language translation of a non-English language foreign application is not required except:
(i) When the application is involved in an interference (see § 41.202 of this chapter) or derivation (see part 42 of this chapter) proceeding;
(ii) When necessary to overcome the date of a reference relied upon by the examiner; or
(iii) When specifically required by the examiner.
(4) If an English language translation of a non-English language foreign application is required, it must be filed together with a statement that the translation of the certified copy is accurate” (emphasis added).
Since an English language translation of Application No. CN202110704382.6 has not been made of record to-date, the Examiner notes that prior art references with a filing date or a publication date prior to the parent PCT Application’s filing date of 06/24/2022 are considered applicable prior art references.
Information Disclosure Statement
Acknowledgment is made of the information disclosure statement filed 07/26/2024, which complies with 37 CFR 1.97. As such, the information disclosure statement has been placed in the application file and the information referred to therein has been considered by the examiner.
Drawings
The drawings are objected to as failing to comply with 37 CFR 1.84(p)(3) because Figures 1-12 include letters which do not measure at least .32 cm. (1/8 inch) in height (i.e., most of the lowercase characters in FIGs. 1-12 and the subscript characters in FIG. 8).
The drawings are also objected to as failing to comply with 37 CFR 1.84(p)(5) because they include the following reference characters not mentioned in the description:
512 (see, e.g., paragraphs 100-103 describing FIG. 5); and
1209 (see, e.g., paragraphs 160-162 describing FIG. 12)
Corrected drawing sheets in compliance with 37 CFR 1.121(d) are required in reply to the Office action to avoid abandonment of the application. Any amended replacement drawing sheet should include all of the figures appearing on the immediate prior version of the sheet, even if only one figure is being amended. The figure or figure number of an amended drawing should not be labeled as “amended.” If a drawing figure is to be canceled, the appropriate figure must be removed from the replacement sheet, and where necessary, the remaining figures must be renumbered and appropriate changes made to the brief description of the several views of the drawings for consistency. Additional replacement sheets may be necessary to show the renumbering of the remaining figures. Each drawing sheet submitted after the filing date of an application must be labeled in the top margin as either “Replacement Sheet” or “New Sheet” pursuant to 37 CFR 1.121(d). If the changes are not accepted by the examiner, the applicant will be notified and informed of any required corrective action in the next Office action. The objection to the drawings will not be held in abeyance.
Specification
The disclosure1 is objected to because of the following informalities:
Reference character 512 shown in Figure 5 is not described in applicant’s specification (see, e.g., paragraphs 100-103 describing FIG. 5) and reference character 1209 shown in Figure 12 is not described in applicant’s specification (see, e.g., paragraphs 160-162 describing FIG. 12). Appropriate correction is required.
In the first sentence of paragraph 80 “a form of a mask (mask)” includes a duplicate word “mask” and it appears this recitation should read “a form of a mask
The title of the invention is objected to as not being descriptive. A new title is required that is clearly indicative of the invention to which the claims are directed. In particular, the title of the invention is “METHOD, APPARATUS, AND SYSTEM FOR GENERATING NEURAL NETWORK MODEL, DEVICE, MEDIUM, AND PROGRAM PRODUCT”; however the title includes grammatical errors. In particular, it appears that the articles “A” is missing prior to the recitations of “NEURAL NETWORK MODEL”. The examiner suggests that one way to address this objection would be to amend the title to read “METHOD, APPARATUS, AND SYSTEM FOR GENERATING A NEURAL NETWORK MODEL BASED ON A SUBNETWORK MODEL AND A HYPERNETWORK MODEL
A descriptive title indicative of the invention will help in proper indexing, classifying, searching, etc. See, MPEP § 606.01. However, the title of the invention should be limited to 500 characters. The examiner suggests including the aspect(s) of the claims which Applicant believes to be novel or nonobvious over the prior art.
Claim Objections
Claims 4-8 and 19 are objected to because of the following informalities:
In claims 4 and 19, it appears the recitations of “a hypernetwork model” (see, line 3 of claims 4 and 19) should recite “the [[a]] hypernetwork model” to unambiguously refer to the “hypernetwork model” previously-introduced in base claims 1 and 17 (and in intervening claims 2 and 18). Appropriate correction is required.
Also, claims 5-8, which each depend directly or indirectly from claim 4, are objected to based on their respective dependencies from claim 4.
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f):
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f). The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f), is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f). The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f), is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f), except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f), except as otherwise indicated in an Office action.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f), because the claim limitations use a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitations are:
a sending unit, configured to send an indication about a structure of a subnetwork model to a second device … wherein the sending unit is further configured to send a parameter of the trained subnetwork model to the second device … ;
a receiving unit, configured to receive a parameter of the subnetwork model from the second device … ; and
a training unit, configured to train the subnetwork model of claim 17;
wherein the receiving unit is further configured to obtain a preconfigured parameter and
a model determining unit, configured to determine the structure of the subnetwork model in claim 18;
wherein the model determining unit is further configured to: initialize the subnetwork model to a hypernetwork model with the preconfigured parameter; and iteratively update the subnetwork model in claim 19;
a receiving unit, configured to receive an indication about a structure of a subnetwork model … wherein the receiving unit is further configured to receive a parameter of the trained subnetwork model;
a unit for determining a parameter of a subnetwork model, configured to determine a parameter of the subnetwork model;
a sending unit, configured to send the parameter of the subnetwork model to the plurality of first devices; and
a hypernetwork update unit, wherein … the hypernetwork update unit is configured to update the hypernetwork model in claim 20.
Regarding claim 17 and the above-noted three-prong test, the recited sending unit is a generic placeholder, configured to send an indication about a structure of a subnetwork model to a second device … wherein the sending unit is further configured to send a parameter of the trained subnetwork model to the second device is functional language, and there is no recitation in claim 17 of sufficient structure to perform the sending. Also in claim 17, the recited receiving unit, is a generic placeholder, configured to receive a parameter of the subnetwork model from the second device is functional language, and there is no recitation in claim 17 of sufficient structure to perform the receiving. Lastly in claim 17, the training unit is a generic placeholder, configured to train the subnetwork model is functional language, and there is no recitation in claim 17 of sufficient structure to perform the training.
Regarding claim 18 and the above-noted three-prong test, the recited receiving unit is a generic placeholder, configured to obtain a preconfigured parameter is functional language, and there is no recitation in claim 18 of sufficient structure to perform the obtaining. Also in claim 18, the recited model determining unit is a generic placeholder, configured to determine the structure of the subnetwork model is functional language, and there is no recitation in claim 18 of sufficient structure to perform the determining.
Regarding claim 18 and the above-noted three-prong test, the recited model determining unit is a generic placeholder, configured to: initialize the subnetwork model to a hypernetwork model with the preconfigured parameter; and iteratively update the subnetwork model is functional language, and there is no recitation in claim 19 of sufficient structure to perform the initializing and iterative updating.
Regarding claim 20 and the above-noted three-prong test, the recited receiving unit is a generic placeholder, configured to receive an indication about a structure of a subnetwork model … wherein the receiving unit is further configured to receive a parameter of the trained subnetwork model is functional language, and there is no recitation in claim 20 of sufficient structure to perform the receiving. Also in claim 20, the recited unit for determining a parameter is a generic placeholder, configured to determine a parameter of the subnetwork model is functional language, and there is no recitation in claim 20 of sufficient structure to perform the determining. Additionally in claim 20, the sending unit is a generic placeholder, configured to send the parameter of the subnetwork model to the plurality of first devices is functional language, and there is no recitation in claim 20 of sufficient structure to perform the sending. Lastly in claim 20, the hypernetwork update unit is a generic placeholder, configured to update the hypernetwork model is functional language, and there is no recitation in claim 20 of sufficient structure to perform the updating.
Because these claim limitations are being interpreted under 35 U.S.C. 112(f), they are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
A review of the specification shows that the corresponding structure is not described in the specification for the 35 U.S.C. 112(f) limitations:
Regarding the above-noted units recited in claims 17, 18, 19 and 20, although the units are depicted in the black box block diagrams of FIGs. 10 and 11 and generally mentioned in paragraphs 21-24, 29-30 and 32-36 (which merely repeat the claim language) and 143-147, 151, 153 and 155 (which generally describe the aforementioned figures), the corresponding structure of the claimed units capable of performing the claimed functions is not described in applicant’s specification.
The drawings merely show black-boxes designed to perform the entire claimed function (see, e.g., “Sending unit” 1010 and 1130, “Receiving unit” 1020 and 1110, “Training unit” 1030, “Model determining unit” 1040, “Unit for determining a parameter of a subnetwork model” 1120 and “Hypernetwork update unit” 1140 shown in FIGs. 10 and 11).
As such, the specification either fails to describe the claimed units as noted above, or, at best, describes the claimed units by their respective functions without disclosing any specific structure performing the claimed functions.
Accordingly, for these claim limitations, the written description fails to disclose both an algorithm(s) and special-purpose computer hardware to perform the algorithm(s). For more information, see MPEP § 2181.
If applicant wishes to provide further explanation or dispute the examiner's interpretation of the corresponding structure, applicant must identify the corresponding structure with reference to the specification by page and line number, and to the drawing, if any, by reference characters in response to this Office action.
If applicant does not intend to have these limitations interpreted under 35 U.S.C. 112(f), applicant may: (1) amend the claim limitations to avoid them being interpreted under 35 U.S.C. 112(f) (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitations recite sufficient structure to perform the claimed function so as to avoid them being interpreted under 35 U.S.C. 112(f).
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.
connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 17-20 are rejected under 35 U.S.C. 112(a) as failing to comply with the written description requirement.
Independent claims 17 and 20 and dependent claims 18-19 each contain 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, at the time the application was filed, had possession of the claimed invention.
In particular, and as previously noted, the claim limitations a sending unit, configured to send an indication about a structure of a subnetwork model to a second device … wherein the sending unit is further configured to send a parameter of the trained subnetwork model to the second device … ; a receiving unit, configured to receive a parameter of the subnetwork model from the second device … ; and a training unit, configured to train the subnetwork model in independent claim 17 invoke 35 U.S.C. 112(f).
In particular, and as also noted above, the claim limitations wherein the receiving unit is further configured to obtain a preconfigured parameter and a model determining unit, configured to determine the structure of the subnetwork model in claim 18 invoke 35 U.S.C. 112(f).
As further noted above, the claim limitations wherein the model determining unit is further configured to: initialize the subnetwork model to a hypernetwork model with the preconfigured parameter; and iteratively update the subnetwork model in claim 19 invoke 35 U.S.C. 112(f).
Lastly, as also previously noted, the claim limitations a receiving unit, configured to receive an indication about a structure of a subnetwork model … wherein the receiving unit is further configured to receive a parameter of the trained subnetwork model; a unit for determining a parameter of a subnetwork model, configured to determine a parameter of the subnetwork model; a sending unit, configured to send the parameter of the subnetwork model to the plurality of first devices; and a hypernetwork update unit, wherein … the hypernetwork update unit is configured to update the hypernetwork model in independent claim 20 invoke 35 U.S.C. 112(f).
However, as noted above, the written description of the current application fails to disclose the corresponding structure, material, or acts for performing each of the above-identified claimed functions and to clearly link the structure, material, or acts to the function. In particular, for each of the claimed functions, the written description fails to disclose both an algorithm(s) and special-purpose computer hardware to perform the algorithm. For more information, see MPEP § 2181.
Accordingly, claims 17, 18, 19 and 20 are rejected under 35 U.S.C. 112(a) as failing to comply with the written description requirement.
Claim 19 which depends directly from claim 18 is rejected under 35 U.S.C. 112(a) is rejected under 35 U.S.C. 112(a) as failing to comply with the written description requirement under the same rationale as claim 18.
Also, claims 18-19 which each depend directly or indirectly from claim 17, are rejected under 35 U.S.C. 112(a) as failing to comply with the written description requirement under the same rationale as claim 17.
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.
Claims 17-20 are rejected under 35 U.S.C. 112(b) as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor regards as the invention.
As discussed above, the claim limitations a sending unit, configured to send an indication about a structure of a subnetwork model to a second device … wherein the sending unit is further configured to send a parameter of the trained subnetwork model to the second device … ; a receiving unit, configured to receive a parameter of the subnetwork model from the second device … ; and a training unit, configured to train the subnetwork model in independent claim 17 invoke 35 U.S.C. 112(f).
As also noted above, the claim limitations wherein the receiving unit is further configured to obtain a preconfigured parameter and a model determining unit, configured to determine the structure of the subnetwork model in claim 18 invoke 35 U.S.C. 112(f).
As further noted above, the claim limitations wherein the model determining unit is further configured to: initialize the subnetwork model to a hypernetwork model with the preconfigured parameter; and iteratively update the subnetwork model in claim 19 invoke 35 U.S.C. 112(f).
Lastly, as also discussed above, the claim limitations a receiving unit, configured to receive an indication about a structure of a subnetwork model … wherein the receiving unit is further configured to receive a parameter of the trained subnetwork model; a unit for determining a parameter of a subnetwork model, configured to determine a parameter of the subnetwork model; a sending unit, configured to send the parameter of the subnetwork model to the plurality of first devices; and a hypernetwork update unit, wherein … the hypernetwork update unit is configured to update the hypernetwork model in claim 20 invoke 35 U.S.C. 112(f).
However, as also discussed above with regard to the rejections of claims 17, 18, 19 and 20 under 35 U.S.C. 112(a), the written description fails to disclose the corresponding structure, material, or acts for performing the entire claimed function and to clearly link the structure, material, or acts to the function. In particular, the specification fails to clearly link the structure, material, or acts to the function for the limitations a sending unit, configured to send an indication about a structure of a subnetwork model to a second device … wherein the sending unit is further configured to send a parameter of the trained subnetwork model to the second device … ; a receiving unit, configured to receive a parameter of the subnetwork model from the second device … ; and a training unit, configured to train the subnetwork model in claim 17.
The specification also fails to clearly link the structure, material, or acts to the function for the limitations wherein the receiving unit is further configured to obtain a preconfigured parameter and a model determining unit, configured to determine the structure of the subnetwork model in claim 18.
The specification also fails to clearly link the structure, material, or acts to the function for the limitations wherein the model determining unit is further configured to: initialize the subnetwork model to a hypernetwork model with the preconfigured parameter; and iteratively update the subnetwork model in claim 19.
The specification additionally fails to clearly link the structure, material, or acts to the function for the limitations a receiving unit, configured to receive an indication about a structure of a subnetwork model … wherein the receiving unit is further configured to receive a parameter of the trained subnetwork model; a unit for determining a parameter of a subnetwork model, configured to determine a parameter of the subnetwork model; a sending unit, configured to send the parameter of the subnetwork model to the plurality of first devices; and a hypernetwork update unit, wherein … the hypernetwork update unit is configured to update the hypernetwork model in claim 20.
As further noted above, there is insufficient disclosure in the specification of algorithms and specific computer hardware for implementing the above-noted, claimed units. As such, the above-noted limitations recited in claims 17-20 are indefinite. Therefore, claims 17-20 are indefinite and are rejected under 35 U.S.C. 112(b). For the purposes of determining patent eligibility and comparison with the prior art, the examiner is interpreting the above-listed units as any combination of software (i.e., a set of instructions, code, one or more functions or software modules) and/or hardware (i.e., circuitry and/or hardware logic components/modules) capable of performing the claimed functions.
Claim 19, which depends directly from claim 18, is rejected under 35 U.S.C. 112(b) as being indefinite under the same rationale as claim 18.
Also, claims 18-19 which each depend directly or indirectly from claim 17 are rejected under 35 U.S.C. 112(b) as being indefinite under the same rationale as claim 17.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
When considering subject matter eligibility under 35 U.S.C. 101, it must be determined whether the claim is directed to one of the four statutory categories of invention, i.e., process, machine, manufacture, or composition of matter (Step 1). If the claim does fall within one of the statutory categories, the second step in the analysis is to determine whether the claim is directed to a judicial exception (Step 2A). The Step 2A analysis is broken into two prongs. In the first prong (Step 2A, Prong 1), it is determined whether or not the claims recite a judicial exception (e.g., mathematical concepts, mental processes, certain methods of organizing human activity). If it is determined in Step 2A, Prong 1 that the claims recite a judicial exception, the analysis proceeds to the second prong (Step 2A, Prong 2), where it is determined whether or not the claims integrate the judicial exception into a practical application. If it is determined at step 2A, Prong 2 that the claims do not integrate the judicial exception into a practical application, the analysis proceeds to determining whether the claim is a patent-eligible application of the exception (Step 2B). If an abstract idea is present in the claim, any element or combination of elements in the claim must be sufficient to ensure that the claim integrates the judicial exception into a practical application, or else amounts to significantly more than the abstract idea itself.
Regarding independent claims 1 and 17, these claims are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 1 is directed to a method, corresponding to a process, and claim 17 is directed to an apparatus, corresponding to an article of manufacture, which are both one of the statutory categories.
Step 2A Prong One Analysis: The claims are directed to an abstract idea. In particular, the claims recite mental processes that are concepts performed in the human mind (including an observation, evaluation, judgment, opinion).
The claims both recite: the subnetwork model is determined by adjusting a structure of a hypernetwork model; … and
the parameter of the subnetwork model is determined by the second device based on the indication and the hypernetwork model.
Under their broadest reasonable interpretation (BRI), in light of the specification, the determining and adjusting limitations encompass the mental processes of creating/determining a structure of a generically-recited hypernetwork model based on observed data, determining/identifying a parameter value of a generically-recited subnetwork model based on a received/observed indication and observing the structure of the hypernetwork model, which are acts of evaluation (i.e., observation of data to adjust structure of a model and then evaluation, judgment, opinion to determine/identify a parameter value).
That is, other than language reciting generic computer components (i.e., “a second device”, “a first device” of claim 1 and the units of claim 17, and using a generic computer programmed with a generic class of computer algorithm, “a neural network model”, “a subnetwork model” and “a hypernetwork model”), the above limitations in the context of these claims encompass creating/determining a structure of a hypernetwork model based on observed data and determining/identifying a parameter of a subnetwork model based on a received/observed indication and observing the hypernetwork model (i.e., evaluation, judgement, opinion), which are considered mental processes.
Regarding the “neural network model”, “subnetwork model” and “hypernetwork model” limitations, no details of models are recited and the models give the indication that they can be constructed and adjusted or modified by hand with pen and paper based on observed and received/given data and evaluation/judgement to adjust/modify a model parameter based on the data (i.e., evaluation/judgement/opinion). Given a sufficiently small set of data, such determining and adjusting can be done with pen and paper.
The “neural network model”, “subnetwork model” and “hypernetwork model” are each recited at a high level of generality and therefore are being interpreted as performing a mental process on a generic computer. See MPEP 2106.04(a)(2) § III.C which states that “a concept that is performed in the human mind and applicant is merely claiming that concept performed 1) on a generic computer, or 2) in a computer environment, or 3) is merely using a computer as a tool to perform the concept” still recite a mental process.
If the claim limitations, under their broadest reasonable interpretations, cover performance of the limitations in the mind but for the recitation of generic computer components (i.e., the “second device”, the “first device” of claim 1, and the units and “apparatus” of claim 17), then they fall within the “Mental Processes” grouping of abstract ideas. Accordingly, claims 1 and 17 recite an abstract idea.
Step 2A Prong Two Analysis:
This judicial exception is not integrated into a practical application.
In particular, the claims recite, using respective similar language, these additional elements –
sending, by a first device, an indication about a structure of a subnetwork model to a second device … ;
receiving, by the first device, a parameter of the subnetwork model from the second device … ; and
sending, by the first device, a parameter of the trained subnetwork model to the second device for the second device to update the hypernetwork model - These limitations are adding insignificant extra-solution activities (amount to necessary data gathering and data outputting) to the judicial exception, as discussed in MPEP § 2106.05(g). Also, the 2nd sending limitation includes intended use language with no patentable weight (e.g., “for the second device to update the hypernetwork model”).
The claims also recite, using respective similar language, the additional limitation of training, by the first device, the subnetwork model based on the received parameter of the subnetwork model; - The above-noted additional element in the claim amounts to recitation of the words "apply it" (or an equivalent) or are mere instructions to implement an abstract idea or other exception on a computer, which does not integrate a judicial exception into a practical application. See MPEP 2106.05(f). In particular, “training, by the first device, the subnetwork model based on the received parameter of the subnetwork model” is simply generic training to perform the abstract idea of processing received parameter data to create a generically-recited subnetwork model for updating/modifying the generically-recited hypernetwork model and amounts to mere instructions to apply the exception (MPEP 2106.05(f)).
Merely asserting that a judicial exception is to be carried out on a generic computer (i.e., with the generically-recited models and the 2nd “device”, the 1st “device” of claim 1, and the units and “apparatus” of claim 17) cannot meaningfully integrate the judicial exception into a practical application. See MPEP § 2106.05(f).
Claim 17 recites the additional elements of “a sending unit, configured to send an indication about a structure of a subnetwork model to a second device … wherein the sending unit is further configured to send a parameter of the trained subnetwork model to the second device … ; a receiving unit, configured to receive a parameter of the subnetwork model from the second device … ; and a training unit, configured to train the subnetwork model”. As discussed above in the section 112(b) rejection of this claim, the units have been interpreted as any combination of software (i.e., a set of instructions, code, one or more functions or software modules) and/or hardware (i.e., circuitry and/or hardware logic components/modules) capable of performing the claimed functions. Thus, the above-noted additional unit elements in the claim amount to recitation of the words "apply it" (or an equivalent) or are mere instructions to implement an abstract idea or other exception on a computer, which does not integrate a judicial exception into a practical application. See MPEP 2106.05(f)
Lastly, the recitations in the preambles of claims 1 and 17 of “A method for generating a neural network model” and “An apparatus for generating a neural network model”, respectively, is intended use language with no patentable weight because aside from these recitations, no “neural network model” let alone any operations or steps “for generating a neural network model” are recited elsewhere in the claims or any of their respective dependent claims. Instead, the method steps of claim 1 and the operations of claim 17 only refer to a hypernetwork model, a subnetwork model and a training of subnetwork model without referring to a "neural network”.
Step 2B Analysis: The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception.
As discussed above with respect to the integration of the abstract idea into a practical application, the additional element of:
“training, by the first device, the subnetwork model based on the received parameter of the subnetwork model” is generic training to perform the abstract idea and amounts to no more than mere instructions to apply the exception (MPEP 2106.05(f)).
Mere instructions to apply the mental process electronically (i.e., with the generically-recited models, 2nd “device”, the 1st “device” of claim 1, and the units and “apparatus” of claim 17) do not amount to significantly more than the judicial exception. As noted above, merely asserting that a judicial exception is to be carried out on a generic computer cannot provide significantly more than the judicial exception. See MPEP § 2106.05(f).
Moreover, receiving, communicating, forwarding and storing data are insignificant extra-solution activities that are well-understood, routine, and conventional. See MPEP2106.05(d)(II) (“The courts have recognized the following computer functions as well-understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity. i. Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); … iv. Storing and retrieving information in memory”) (citing OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015)) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network)". Therefore, the recitations, using respective similar language, of “sending, by a first device, an indication about a structure of a subnetwork model to a second device … ; receiving, by the first device, a parameter of the subnetwork model from the second device … ; and sending, by the first device, a parameter of the trained subnetwork model to the second device” are the well-understood, routine, conventional activities of receiving and transmitting data over a network, as discussed in MPEP § 2106.05(d).
Accordingly, at Step 2B, the additional elements do not amount to significantly more than the judicial exception.
As an ordered whole, the claims are directed to a method of creating/determining a structure of a generically-recited hypernetwork model based on observed data and then determining/identifying a parameter value of a generically-recited subnetwork model based on a received/observed indication and observing the structure of the hypernetwork model. Nothing in the claims provide significantly more than this.
The additional elements do not provide an inventive concept, and, therefore, the claims are not patent eligible.
Regarding independent claims 11 and 20, these claims are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 11 is directed to a method, corresponding to a process, claim 11 is directed to a device, corresponding to an article of manufacture, and claim 20 is directed to an apparatus, corresponding to an article of manufacture, which are both one of the statutory categories.
Step 2A Prong One Analysis: The claims are directed to an abstract idea. In particular, the claims recite mental processes that are concepts performed in the human mind (including an observation, evaluation, judgment, opinion).
The claims recite, using respective similar language:
the subnetwork model is determined by adjusting a structure of a hypernetwork model;
determining … a parameter of the subnetwork model based on the indication and the hypernetwork model; … and
updating … the hypernetwork model by using the received parameter.
Under their BRI, in light of the specification, the adjusting, determining and updating limitations encompass the mental processes of creating/determining a generically-recited subnetwork model by adjusting/modifying a structure of a generically-recited hypernetwork model based on observed data and determining/identifying a parameter value of the subnetwork model based on a received/observed indication and observing the structure of the hypernetwork model, and then updating/modifying the hypernetwork model by using the received/observed parameter, which are acts of evaluation (i.e., observation of data to adjust a structure of a model, evaluation, judgment, opinion to determine/identify a parameter value and then updating/modifying the model based on the received/observed parameter).
That is, other than language reciting generic computer components (i.e., “a plurality of first devices”, “a second device” of claim 11 and the units and “apparatus” of claim 20, and using a generic computer programmed with a generic class of computer algorithm, “a neural network model”, “a subnetwork model” and “a hypernetwork model”), the above limitations in the context of these claims encompass creating/determining a subnetwork model by adjusting/modifying a structure of a hypernetwork model based on observed data and determining/identifying a parameter value of the subnetwork model based on a received/observed indication and observing the structure of the hypernetwork model, and then updating/modifying the hypernetwork model by using the received/observed parameter (i.e., evaluation, judgement, opinion), which are considered mental processes.
Regarding the “neural network model”, “subnetwork model” and “hypernetwork model” limitations, no details of models are recited and the models give the indication that they can be constructed and adjusted or modified by hand with pen and paper based on observed and received/given data by using evaluation/judgement to adjust/modify a structure of a model, identify/determine a parameter based on an observed indication and by observing a hypernetwork model, and then update the hypernetwork model using the received/observed parameter (i.e., evaluation/judgement/opinion). Given a sufficiently small set of data, such adjusting, determining and updating can be done with pen and paper.
The “neural network model”, “subnetwork model” and “hypernetwork model” are each recited at a high level of generality and therefore are being interpreted as performing a mental process on a generic computer. See MPEP 2106.04(a)(2) § III.C which states that “a concept that is performed in the human mind and applicant is merely claiming that concept performed 1) on a generic computer, or 2) in a computer environment, or 3) is merely using a computer as a tool to perform the concept” still recite a mental process.
If the claim limitations, under their broadest reasonable interpretations, cover performance of the limitations in the mind but for the recitation of generic computer components (i.e., the plurality of 1st devices, the models, the 2nd device of claim 11, and the units and “apparatus” of claim 20), then they fall within the “Mental Processes” grouping of abstract ideas. Accordingly, claims 11 and 20 recite an abstract idea.
Step 2A Prong Two Analysis:
This judicial exception is not integrated into a practical application.
In particular, the claims recite, using respective similar language, these additional elements –
receiving, by a second device, an indication about a structure of a subnetwork model from a plurality of first devices … ;
sending, by the second device, the parameter of the subnetwork model to the plurality of first devices for the plurality of first devices to separately train the subnetwork model;
receiving, by the second device, a parameter of the trained subnetwork model from the plurality of first devices;
These limitations are adding insignificant extra-solution activities (amount to necessary data gathering and data outputting) to the judicial exception, as discussed in MPEP § 2106.05(g). Also, the sending limitation includes intended use language with no patentable weight (e.g., “to separately train the subnetwork model”).
The claims also recite the additional limitation of to separately train the subnetwork model; - As noted above, this additional element in the claims is intended use language with no patentable weight. Aside from this recitation, there is no positive recitation of any separate training of “the subnetwork model”, let alone any operations or steps “to separately train the subnetwork model” elsewhere in the claims or any claims depending from claim 11 (claim 20 has no dependent claims).
Even assuming arguendo that the limitation positively recites training the model, which it does not, the limitation amounts to recitation of the words "apply it" (or an equivalent) or are mere instructions to implement an abstract idea or other exception on a computer, which does not integrate a judicial exception into a practical application. See MPEP 2106.05(f). In particular, “separately train the subnetwork model” is simply generic training to perform the abstract idea of processing received parameter data to create a generically-recited subnetwork model for updating/modifying the generically-recited hypernetwork model and amounts to mere instructions to apply the exception (MPEP 2106.05(f)).
Merely asserting that a judicial exception is to be carried out on a generic computer (i.e., with the generically-recited models, the plurality of 1st devices, the 2nd device of claim 11, and the “apparatus” of claim 20) cannot meaningfully integrate the judicial exception into a practical application. See MPEP § 2106.05(f).
Claim 20 recites the additional elements of “a receiving unit, configured to receive an indication about a structure of a subnetwork model … wherein the receiving unit is further configured to receive a parameter of the trained subnetwork model; a unit for determining a parameter of a subnetwork model, configured to determine a parameter of the subnetwork model; a sending unit, configured to send the parameter of the subnetwork model to the plurality of first devices; and a hypernetwork update unit, wherein … the hypernetwork update unit is configured to update the hypernetwork model”. As discussed above in the section 112(b) rejection of this claim, the units have been interpreted as any combination of software (i.e., a set of instructions, code, one or more functions or software modules) and/or hardware (i.e., circuitry and/or hardware logic components/modules) capable of performing the claimed functions. Thus, the above-noted additional unit elements in the claim amount to recitation of the words "apply it" (or an equivalent) or are mere instructions to implement an abstract idea or other exception on a computer, which does not integrate a judicial exception into a practical application. See MPEP 2106.05(f)
Further, the recitations in the preambles of claims 11 and 20 of “A method for generating a neural network model” and “An apparatus for generating a neural network model”, respectively, is intended use language with no patentable weight because aside from these recitations, no “neural network model” let alone any operations or steps “for generating a neural network model” are recited elsewhere in the claims or any of claim 11’s dependent claims (claim 20 has no dependent claims). Instead, the method steps of claim 11 and the operations of claim 20 only refer to a hypernetwork model and a subnetwork model without referring to a "neural network”.
Step 2B Analysis: The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception.
As discussed above with respect to the integration of the abstract idea into a practical application, the additional element of:
“separately train the subnetwork model” is generic training to perform the abstract idea and amounts to no more than mere instructions to apply the exception (MPEP 2106.05(f)).
Mere instructions to apply the mental process electronically (i.e., with the generically-recited models, “plurality of first devices”, the “second device” of claim 11, and the units and “apparatus” of claim 20) do not amount to significantly more than the judicial exception. As noted above, merely asserting that a judicial exception is to be carried out on a generic computer cannot provide significantly more than the judicial exception. See MPEP § 2106.05(f).
Moreover, receiving, communicating, forwarding and storing data are insignificant extra-solution activities that are well-understood, routine, and conventional. See MPEP2106.05(d)(II) (“The courts have recognized the following computer functions as well-understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity. i. Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); … iv. Storing and retrieving information in memory”) (citing OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015)) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network)". Therefore, the recitations, using respective similar language, of “receiving, by a second device, an indication about a structure of a subnetwork model from a plurality of first devices … ; sending, by the second device, the parameter of the subnetwork model to the plurality of first devices for the plurality of first devices to separately train the subnetwork model; receiving, by the second device, a parameter of the trained subnetwork model from the plurality of first devices” are the well-understood, routine, conventional activities of receiving and transmitting data over a network, as discussed in MPEP § 2106.05(d).
Accordingly, at Step 2B, the additional elements do not amount to significantly more than the judicial exception.
As an ordered whole, the claims are directed to a method of creating/determining a generically-recited subnetwork model by adjusting/modifying a structure of a generically-recited hypernetwork model based on observed data, determining/identifying a parameter value of the subnetwork model based on a received/observed indication and observing the structure of the hypernetwork model, and then updating/modifying the hypernetwork model by using the received/observed parameter. Nothing in the claims provide significantly more than this.
The additional elements do not provide an inventive concept, and, therefore, the claims are not patent eligible.
Regarding claims 2 and 18, these claims are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 2 is directed to method as depending from claim 1 and claim 18 is directed to an apparatus as depending from claim 17, thus the analysis for patent eligibilities of claims 1 and 17 are incorporated herein.
Step 2A Prong One Analysis: The claims are directed to an abstract idea. In particular, the claims recite mental processes that are concepts performed in the human mind (including an observation, evaluation, judgment, opinion).
The claims recite, using respective similar language:
determining … the structure of the subnetwork model based on the preconfigured parameter and by adjusting the structure of the hypernetwork model - Under their BRI, in light of the specification, the determining and adjusting limitations encompass the mental processes of determining/identifying the structure of the generically-recited subnetwork model based on received/observed data (the preconfigured parameter) and by adjusting/modifying the structure of the generically-recited hypernetwork model, which are acts of evaluation (i.e., evaluation, judgment, opinion to determine structure of a model based on observation of data/parameter values and by adjusting the structure of another model).
That is, other than language reciting generic computer components (i.e., “the first device” of claim 2 and the units and “apparatus” of claim 18, and using a generic computer programmed with a generic class of computer algorithm, “the subnetwork model” and “the hypernetwork model”), the above limitations in the context of these claims encompass creating/determining a structure of the subnetwork model based on observed parameter data and by adjusting/modifying structure of the hypernetwork model (i.e., evaluation, judgement, opinion), which are considered mental processes.
Regarding the “subnetwork model” and “hypernetwork model” limitations, no details of models are recited and the models give the indication that their structures can be determined and adjusted/modified by hand with pen and paper based on observed and received data/parameters (i.e., evaluation/judgement/opinion to adjust/modify model structures based on parameters/data). Given a sufficiently small set of parameters/data, such determining and adjusting can be done with pen and paper.
The “subnetwork model” and “hypernetwork model” are both recited at a high level of generality and therefore are being interpreted as performing a mental process on a generic computer. See MPEP 2106.04(a)(2) § III.C which states that “a concept that is performed in the human mind and applicant is merely claiming that concept performed 1) on a generic computer, or 2) in a computer environment, or 3) is merely using a computer as a tool to perform the concept” still recite a mental process.
If the claim limitations, under their broadest reasonable interpretations, cover performance of the limitations in the mind but for the recitation of generic computer components (i.e., with the generically-recited models, “the first device” of claim 2, and the units and “apparatus” of claim 18), then they fall within the “Mental Processes” grouping of abstract ideas. Accordingly, claims 2 and 18 recite an abstract idea.
Step 2A Prong Two Analysis:
This judicial exception is not integrated into a practical application.
In particular, the claims recite, using respective similar language, this additional element – obtaining … a preconfigured parameter of the hypernetwork model - This limitation is adding insignificant extra-solution activity (amounts to necessary data gathering) to the judicial exception, as discussed in MPEP § 2106.05(g).
The claims also recite, the additional limitations of by the first device (claim 2), wherein the receiving unit is further configured to obtain, and a model determining unit, configured to determine the structure (claim 18). The above-noted additional elements in the claims amount to recitations of the words "apply it" (or an equivalent) or are mere instructions to implement an abstract idea or other exception on a computer, which do not integrate a judicial exception into a practical application. See MPEP 2106.05(f).
As discussed above in the section 112(b) rejection of claim 18, the units recited in the claim have been interpreted as any combination of software (i.e., a set of instructions, code, one or more functions or software modules) and/or hardware (i.e., circuitry and/or hardware logic components/modules) capable of performing the claimed functions. Thus, the above-noted additional unit elements in the claim amount to recitation of the words "apply it" (or an equivalent) or are mere instructions to implement an abstract idea or other exception on a computer, which does not integrate a judicial exception into a practical application. See MPEP 2106.05(f)
Merely asserting that a judicial exception is to be carried out on a generic computer (i.e., with the generically-recited models, “the first device” of claim 2, and the units and “apparatus” of claim 18) cannot meaningfully integrate the judicial exception into a practical application. See MPEP § 2106.05(f).
Step 2B Analysis: The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception.
As discussed above with respect to the integration of the abstract idea into a practical application, the additional elements of:
by the first device (claim 2), wherein the receiving unit is further configured to obtain, and a model determining unit, configured to determine the structure (claim 18) amount to no more than mere instructions to apply the exception (MPEP 2106.05(f)).
Mere instructions to apply the mental process electronically (i.e., with the generically-recited models, the 1st “device” of claim 2, and the units and “apparatus” of claim 18) do not amount to significantly more than the judicial exception. As noted above, merely asserting that a judicial exception is to be carried out on a generic computer cannot provide significantly more than the judicial exception. See MPEP § 2106.05(f).
Moreover, receiving, communicating, forwarding and storing data are insignificant extra-solution activities that are well-understood, routine, and conventional. See MPEP2106.05(d)(II) (“The courts have recognized the following computer functions as well-understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity. i. Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); … iv. Storing and retrieving information in memory”) (citing OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015)) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network)". Therefore, the recitation, using respective similar language, of “obtaining … a preconfigured parameter of the hypernetwork model” are the well-understood, routine, conventional activities of receiving and transmitting data over a network, as discussed in MPEP § 2106.05(d).
Accordingly, at Step 2B, the additional elements do not amount to significantly more than the judicial exception.
The additional elements do not provide an inventive concept, and, therefore, the claims are not patent eligible.
Regarding claim 3, this claim is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 3 is directed to method as depending from claim 2, thus the analysis for patent eligibilities of claim 2, and of base claim 1 are incorporated herein.
Step 2A Prong One Analysis: The claim is directed to an abstract idea. In particular, the claim recites mental processes that are concepts performed in the human mind (including an observation, evaluation, judgment, opinion).
The claim recites: determine a local parameter of the hypernetwork model; … wherein the preconfigured parameter is determined by the second device based on at least the local parameter received from the first device - Under their BRI, in light of the specification, the determine limitations encompass the mental processes of determining/identifying a local parameter of the generically-recited hypernetwork model based on received/observed data (a parameter value) and determining the preconfigured parameter based on observed data/the local parameter, which are acts of evaluation (i.e., evaluation, judgment, opinion to determine/identify the local and preconfigured parameters based on observation of data/parameter values).
That is, other than language reciting generic computer components (i.e., the generically-recited hypernetwork model, “the first device”, “the second device”, and using a generic computer programmed with a generic class of computer algorithm, “the hypernetwork model”), the above limitations in the context of this claim encompass determining a local parameter of the hypernetwork model based on received/observed data (a parameter value) and determining the preconfigured parameter based on observed data/the local parameter (i.e., evaluation, judgement, opinion), which are considered mental processes.
Regarding the “hypernetwork model” limitations, no details of model are recited and the model gives the indication that its parameters can be determined by hand with pen and paper based on observed and received data/parameters (i.e., evaluation/judgement/opinion to determine local and preconfigured parameters based on parameters/data values). Given a sufficiently small set of parameter values/data, such determining can be done with pen and paper.
The “hypernetwork model” is recited at a high level of generality and therefore is being interpreted as performing a mental process on a generic computer. See MPEP 2106.04(a)(2) § III.C which states that “a concept that is performed in the human mind and applicant is merely claiming that concept performed 1) on a generic computer, or 2) in a computer environment, or 3) is merely using a computer as a tool to perform the concept” still recite a mental process.
If the claim limitations, under their broadest reasonable interpretations, cover performance of the limitations in the mind but for the recitation of generic computer components (i.e., with the generically-recited model, “the first device”, and “the second device”), then they fall within the “Mental Processes” grouping of abstract ideas. Accordingly, claim 3 recites an abstract idea.
Step 2A Prong Two Analysis:
This judicial exception is not integrated into a practical application.
In particular, the claim recites this additional element –
locally training, by the first device, the hypernetwork model - The above-noted additional element in the claim amounts to recitation of the words "apply it" (or an equivalent) or are mere instructions to implement an abstract idea or other exception on a computer, which does not integrate a judicial exception into a practical application. See MPEP 2106.05(f). In particular, “locally training, by the first device, the hypernetwork model” is simply generic training to perform the abstract idea of processing received parameter data to create a generically-recited hypernetwork model by a generic 1st device and amounts to mere instructions to apply the exception (MPEP 2106.05(f)).
Merely asserting that a judicial exception is to be carried out on a generic computer (i.e., with the generically-recited model and the 1st and 2nd “device” cannot meaningfully integrate the judicial exception into a practical application. See MPEP § 2106.05(f).
The claim also recites these additional elements of:
sending, by the first device, the local parameter to the second device; and
receiving, by the first device, the preconfigured parameter from the second device - These limitations are adding insignificant extra-solution activities (amount to necessary data gathering and data outputting/transmitting) to the judicial exception, as discussed in MPEP § 2106.05(g).
Step 2B Analysis: The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception.
As discussed above with respect to the integration of the abstract idea into a practical application, the additional element of:
“locally training, by the first device, the hypernetwork model” is generic training to perform the abstract idea and amounts to no more than mere instructions to apply the exception (MPEP 2106.05(f)).
Mere instructions to apply the mental process electronically (i.e., with the generically-recited model and 1st and 2nd “device”) do not amount to significantly more than the judicial exception. As noted above, merely asserting that a judicial exception is to be carried out on a generic computer cannot provide significantly more than the judicial exception. See MPEP § 2106.05(f).
Moreover, receiving, communicating, forwarding and storing data are insignificant extra-solution activities that are well-understood, routine, and conventional. See MPEP2106.05(d)(II) (“The courts have recognized the following computer functions as well-understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity. i. Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); … iv. Storing and retrieving information in memory”) (citing OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015)) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network)". Therefore, the recitations of “sending, by the first device, the local parameter to the second device; and receiving, by the first device, the preconfigured parameter from the second device” are the well-understood, routine, conventional activities of receiving and transmitting data over a network, as discussed in MPEP § 2106.05(d).
Accordingly, at Step 2B, the additional elements do not amount to significantly more than the judicial exception.
Regarding claims 4 and 19, these claims are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 4 is directed to method as depending from claim 2 and claim 19 is directed to an apparatus as depending from claim 18, thus the analysis for patent eligibilities of claims 2 and 18, and of base claims 1 and 17 are incorporated herein.
Step 2A Prong One Analysis: The claims are directed to an abstract idea. In particular, the claims recite mental processes that are concepts performed in the human mind (including an observation, evaluation, judgment, opinion).
The claims recite, using respective similar language: wherein determining the structure of the subnetwork model comprises:
initializing the subnetwork model to a hypernetwork2 model with the preconfigured parameter; and …
adjusting a structure of a plurality of layers of the subnetwork model to obtain a plurality of candidate network models; and
selecting a candidate network model based on an accuracy of the plurality of candidate network models to update the subnetwork model;
wherein if the subnetwork model meets a constraint of the first device, the structure of the subnetwork model has been determined - Under their BRI, in light of the specification, the determining, initializing, adjusting and selecting limitations encompass the mental processes of determining/identifying the structure of the generically-recited subnetwork model based on received/observed data (the preconfigured parameter), adjusting/modifying layers of the subnetwork model and updating/modifying the subnetwork model by selecting a candidate network model based on an observed accuracy of candidate models, and determining that the subnetwork model’s structure when the subnetwork model meets the 1st device’s constraint, which are acts of evaluation (i.e., evaluation, judgment, opinion to determine structure of a model based on observation of data/parameter values and by adjusting layers of the model, selecting a candidate model, and deciding if the subnetwork model meets a constraint of the 1st device).
That is, other than language reciting generic computer components (i.e., the generically-recited models, the “first device”, and the “apparatus” of claim 19, and using a generic computer programmed with a generic class of computer algorithm, “subnetwork model”, “candidate network model” and “a hypernetwork model”), the above limitations in the context of these claims encompass creating/determining a structure of a subnetwork model based on observed parameter and constraint data values, and adjusting/modifying layers of the subnetwork model and selecting a candidate network model based on an observed accuracy (i.e., evaluation, judgement, opinion), which are considered mental processes.
Regarding the “subnetwork model”, “candidate network model”, and “hypernetwork model” limitations, aside from the subnetwork model having “a plurality of layers”, no details of models are recited and the models give the indication that their structures can be determined and adjusted/modified by hand with pen and paper based on observed and received data/parameters (i.e., evaluation/judgement/opinion to adjust/modify model structures based on parameters/data and a constraint). Given a sufficiently small set of parameters/data and a device constraint, such determining, adjusting and selecting can be done with pen and paper.
The “candidate network model”, “subnetwork model” and “hypernetwork model” are each recited at a high level of generality and therefore are being interpreted as performing a mental process on a generic computer. See MPEP 2106.04(a)(2) § III.C which states that “a concept that is performed in the human mind and applicant is merely claiming that concept performed 1) on a generic computer, or 2) in a computer environment, or 3) is merely using a computer as a tool to perform the concept” still recite a mental process.
If the claim limitations, under their broadest reasonable interpretations, cover performance of the limitations in the mind but for the recitation of generic computer components (i.e., with the generically-recited models, “the first device”, and the unit and “apparatus” of claim 19), then they fall within the “Mental Processes” grouping of abstract ideas. Accordingly, claims 4 and 19 recite an abstract idea.
Step 2A Prong Two Analysis:
This judicial exception is not integrated into a practical application.
In particular, the claims recite, using respective similar language, the additional element of: iteratively updating the subnetwork model by performing the following operations at least once - which can be characterized as insignificant extra solution activity. See MPEP 2106.05(g). This additional element repeats steps/operations that covers mental processes, as discussed above. In particular, this step of iterating/repeating mental processes corresponds to a mental process which can be done mentally or by pen and paper.
As discussed above in the section 112(b) rejection of claim 19, the unit recited in the claim has been interpreted as any combination of software (i.e., a set of instructions, code, one or more algorithms, functions or software modules) and/or hardware (i.e., circuitry and/or hardware logic components/modules) capable of performing the claimed functions. Thus, the above-noted additional unit element in the claim amount to recitation of the words "apply it" (or an equivalent) or are mere instructions to implement an abstract idea or other exception on a computer, which does not integrate a judicial exception into a practical application. See MPEP 2106.05(f)
Merely asserting that a judicial exception is to be carried out on a generic computer (i.e., with the generically-recited models, “the first device”, and the unit and “apparatus” of claim 19) cannot meaningfully integrate the judicial exception into a practical application. See MPEP § 2106.05(f).
Step 2B Analysis: The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception.
As discussed above with respect to the integration of the abstract idea into a practical application, the additional elements of:
re-execute the generative artificial intelligence model based on the modified input can be characterized as insignificant extra solution activity that is well understood routine and conventional. See MPEP 2106.05(g) and MPEP 2106.05(d)(II) example (ii) provides that performing repetitive calculations has been understood by the courts to be well-understood, routine and conventional.
Mere instructions to apply the mental process electronically (i.e., with the generically-recited models, the 1st “device”, and the unit and “apparatus” of claim 19) do not amount to significantly more than the judicial exception. As noted above, merely asserting that a judicial exception is to be carried out on a generic computer cannot provide significantly more than the judicial exception. See MPEP § 2106.05(f).
Accordingly, at Step 2B, the additional elements do not amount to significantly more than the judicial exception.
The additional elements do not provide an inventive concept, and, therefore, the claims are not patent eligible.
Regarding claim 5, this claim is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 5 is directed to method as depending from claim 4, thus the analysis for patent eligibilities of claim 4, intervening claim 2, and of base claim 1 are incorporated herein.
Step 2A Prong One Analysis: The claim is directed to an abstract idea. In particular, the claim recites mental processes that are concepts performed in the human mind (including an observation, evaluation, judgment, opinion).
The claim recites: wherein the adjusting a structure of a plurality of layers comprises:
deleting parameters related to a plurality of nodes of a layer in the plurality of layers to obtain one of the plurality of candidate network models - Under its BRI, in light of the specification, the deleting limitation encompasses the mental process of determining/identifying a parameter to delete based on received/observed data (a parameter value) to obtain/create a generically-recited candidate network model, which is an act of evaluation (i.e., evaluation, judgment, opinion to determine/identify the parameter related to/associated with nodes in a layer based on observation of data - a parameter value and node data).
That is, other than language reciting generic computer components (i.e., the generically-recited plurality of candidate network models, and using a generic computer programmed with a generic class of computer algorithm, the obtained candidate network model), the above limitation in the context of this claim encompasses deleting a parameter based on received/observed data in order to obtain/create a candidate network model based on received/observed data – a parameter value and nodes of a layer (i.e., evaluation, judgement, opinion), which are considered mental processes.
Regarding the “candidate network models” limitation, no details of models are recited and the models give the indication that one of the models can be obtained/created by hand with pen and paper based on observed and received data/parameters and layer nodes (i.e., evaluation/judgement/opinion to delete parameters based on observed data - parameter values related to nodes in a layer). Given a sufficiently small set of parameter values and nodes, such deleting can be done with pen and paper.
The “candidate network models” are recited at a high level of generality and therefore are being interpreted as performing a mental process on a generic computer. See MPEP 2106.04(a)(2) § III.C which states that “a concept that is performed in the human mind and applicant is merely claiming that concept performed 1) on a generic computer, or 2) in a computer environment, or 3) is merely using a computer as a tool to perform the concept” still recite a mental process.
If the claim limitations, under their broadest reasonable interpretations, cover performance of the limitations in the mind but for the recitation of generic computer components (i.e., with the generically-recited models), then they fall within the “Mental Processes” grouping of abstract ideas. Accordingly, claim 5 recites an abstract idea.
Step 2A Prong Two Analysis: The judicial exceptions are not integrated into a practical application.
The claim does not recite any additional elements that integrate the abstract idea into a practical application or provide significantly more than the abstract idea, and thus the claim is subject-matter ineligible.
Step 2B Analysis: The claim does not recite additional elements that are sufficient to amount to significantly more than the judicial exception.
This claim is not patent eligible.
Regarding claim 6, this claim is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 6 is directed to method as depending from claim 5, thus the analysis for patent eligibilities of claim 5, intervening claims 2 and 4, and of base claim 1 are incorporated herein.
Step 2A Prong One Analysis: The claim is directed to an abstract idea. In particular, the claim recites mental processes that are concepts performed in the human mind (including an observation, evaluation, judgment, opinion).
The claim recites: determining the plurality of nodes based on a predetermined quantity percentage - Under its BRI, in light of the specification, the determining limitation encompasses the mental process of determining/identifying a plurality of nodes based on received/observed data (predetermined quantity percentage), which is an act of evaluation (i.e., evaluation, judgment, opinion to determine/identify the nodes based on observation of data - predetermined quantity percentage and node data).
Accordingly, claim 6 recites an abstract idea.
Step 2A Prong Two Analysis: The judicial exceptions are not integrated into a practical application.
The claim does not recite any additional elements that integrate the abstract idea into a practical application or provide significantly more than the abstract idea, and thus the claim is subject-matter ineligible.
Step 2B Analysis: The claim does not recite additional elements that are sufficient to amount to significantly more than the judicial exception.
This claim is not patent eligible.
Regarding claim 7, this claim is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 7 is directed to method as depending from claim 4, thus the analysis for patent eligibilities of claim 4, intervening claim 2, and of base claim 1 are incorporated herein.
Step 2A Prongs 1-2:
The claim recites: wherein the constraint comprises: a calculation amount of the subnetwork model is less than a first threshold, or a quantity of parameters of the subnetwork model is less than a second threshold - This wherein clause further limits what the constraint includes (i.e., either a calculation amount of the subnetwork model is below a 1st threshold, or a quantity/number of parameters of the subnetwork model is below a 2nd threshold). As discussed above with regard to claim 4, determining the subnetwork model’s structure when the subnetwork model meets the 1st device’s constraint is a mental process (evaluation/judgement/opinion based on an observed constraint).
Merely asserting that a judicial exception is to be carried out on a generic computer (i.e., with the generically-recited “subnetwork model” having “a calculation amount … or a quantity of parameters”) cannot meaningfully integrate the judicial exception into a practical application. See MPEP § 2106.05(f).
This limitation does nothing to alter the fundamental nature of the claim as a mental process. This is because the additional limitation merely limits the invention to a narrower abstract idea by further narrowing what the 1st device’s “constraint” includes.
Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. Thus, this new limitation does nothing to alter the analysis of claims 1, 2 and 4.
The claim is directed to an abstract idea.
Step 2B Analysis: The claim does not recite additional elements that are sufficient to amount to significantly more than the judicial exception.
This claim is not patent eligible.
Regarding claim 8, this claim is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 8 is directed to method as depending from claim 7, thus the analysis for patent eligibilities of claim 7, intervening claims 2 and 4, and of base claim 1 are incorporated herein.
Step 2A Prongs 1-2:
The claim recites: wherein the first threshold and the second threshold are both associated with performance of the first device - This wherein clause further limits what the thresholds are associated with/correspond to (i.e., performance of the generically-recited 1st “device”). As discussed above with regard to claim 4, determining the subnetwork model’s structure when the subnetwork model meets the 1st device’s constraint is a mental process (evaluation/judgement/opinion based on an observed constraint).
Merely asserting that a judicial exception is to be carried out on a generic computer (i.e., with the generically-recited “subnetwork model” having “a calculation amount … or a quantity of parameters”) cannot meaningfully integrate the judicial exception into a practical application. See MPEP § 2106.05(f).
This limitation does nothing to alter the fundamental nature of the claim as a mental process. This is because the additional limitation merely limits the invention to a narrower abstract idea by further narrowing what the 1st device’s “constraint” includes.
Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. Thus, this new limitation does nothing to alter the analysis of claims 1, 2 and 4.
The claim is directed to an abstract idea.
Step 2B Analysis: The claim does not recite additional elements that are sufficient to amount to significantly more than the judicial exception.
This claim is not patent eligible.
Regarding claims 9 and 12, these claims are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claims 9 is directed to method as depending from claim 1, and claim 12 is directed to method as depending from claim 11, thus the analysis for patent eligibilities of claims 1 and 11 are incorporated herein.
Step 2A Prongs 1-2:
The claims both recite: wherein the indication is in a form of a mask indicating whether the subnetwork model has a corresponding parameter of the hypernetwork model. - This wherein clause further limits what form the indication has (i.e., a mask indicating whether the generically-recited subnetwork model has a corresponding parameter of the generically-recited hypernetwork model). As discussed above with regard to base claims 1 and 11, determining/identifying a parameter of the subnetwork model based on a received/observed indication (i.e., evaluation, judgement, opinion), is a mental process (evaluation/judgement/opinion based on observed indication data).
Merely asserting that a judicial exception is to be carried out on a generic computer (i.e., with the generically-recited models) cannot meaningfully integrate the judicial exception into a practical application. See MPEP § 2106.05(f).
This limitation does nothing to alter the fundamental nature of the claims as a mental process. This is because the additional limitation merely limits the invention to a narrower abstract idea by further narrowing what form the indication has (i.e., a data mask or vector),
Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. Thus, this new limitation does nothing to alter the analysis of claims 1 and 11.
The claims are directed to an abstract idea.
Step 2B Analysis: The claims do not recite additional elements that are sufficient to amount to significantly more than the judicial exception.
These claims are not patent eligible.
Regarding claim 10, this claim is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 10 is directed to method as depending from claim 1, thus the analysis for patent eligibility of claim 1 is incorporated herein.
Step 2A Prong One Analysis: The claim is directed to an abstract idea. In particular, the claim recites a mental process that is a concept performed in the human mind (including an observation, evaluation, judgment, opinion) combined with a mathematical concept (i.e., mathematical relationships, mathematical formulas or equations, and mathematical calculations).
The claim recites: determining a change in the parameter by calculating a difference between the parameter of the trained subnetwork model and the parameter received from the second device - Under their BRI, in light of the specification, the determining limitation covers concepts performed in the human mind (evaluation, judgement, or opinion to determine a parameter of a generically-recited model, combined with a mathematical concept - i.e., mathematical relationships, mathematical formulas or equations, and mathematical calculations – to compute/calculate “a difference between the parameter of the trained subnetwork model and the parameter received from the second device.”).
Regarding the “subnetwork model” limitation, no details of model are recited and the model gives the indication that its parameters can be determined by hand with pen and paper based on observed and received data/parameters (i.e., evaluation/judgement/opinion to determine a parameter based on observed parameters/data values). Given a sufficiently small set of parameter values/data, such determining can be done with pen and paper.
The claim limitation, under its BRI, covers performance of the limitations in the mind but for the recitation of generic computer components (i.e., with the generically-recited model and “the second device”), combined with a mathematical concept - mathematical relationships, mathematical formulas or equations, or mathematical calculations (e.g., calculating/computing a difference between the parameter of the trained subnetwork model and the parameter received from the second device).
Therefore, the claim is directed to an abstract idea – a mental process combined with a mathematical concept.
Step 2A Prong Two Analysis:
This judicial exception is not integrated into a practical application.
In particular, the claim recites this additional element –
wherein the sending a parameter of the trained subnetwork model comprises: … sending the change in the parameter to the second device. - This limitation is adding insignificant extra-solution activity (amounts to mere data outputting/transmitting) to the judicial exception, as discussed in MPEP § 2106.05(g).
Merely asserting that a judicial exception is to be carried out on a generic computer (i.e., with the generically-recited model and the 2nd “device” cannot meaningfully integrate the judicial exception into a practical application. See MPEP § 2106.05(f).
Step 2B Analysis: The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Mere instructions to apply the mental process electronically (i.e., with the generically-recited model and the 2nd “device”) do not amount to significantly more than the judicial exception. As noted above, merely asserting that a judicial exception is to be carried out on a generic computer cannot provide significantly more than the judicial exception. See MPEP § 2106.05(f).
Moreover, receiving, communicating, forwarding and storing data are insignificant extra-solution activities that are well-understood, routine, and conventional. See MPEP2106.05(d)(II) (“The courts have recognized the following computer functions as well-understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity. i. Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); … iv. Storing and retrieving information in memory”) (citing OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015)) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network)". Therefore, the recitation of “wherein the sending a parameter of the trained subnetwork model comprises: … sending the change in the parameter to the second device” is the well-understood, routine, conventional activity of transmitting data over a network, as discussed in MPEP § 2106.05(d).
Accordingly, at Step 2B, the additional element does not amount to significantly more than the judicial exception.
Regarding claim 13, this claim is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 13 is directed to method as depending from claim 11, thus the analysis for patent eligibility of claim 11 is incorporated herein.
Step 2A Prongs 1-2:
The claim recites: wherein the receiving a parameter of the trained subnetwork model further comprises: receiving, from the plurality of first devices, a change in the parameter of the trained subnetwork model - This wherein clause further limits what receiving the parameter includes (i.e., receiving, from the 1st devices, a change in the parameter of the trained subnetwork model). As discussed above with regard to claim 11, receiving a parameter of the trained subnetwork model is adding insignificant extra-solution activity (amounts to necessary data gathering) to the judicial exception, as discussed in MPEP § 2106.05(g). Similarly, “receiving, from the plurality of first devices, a change in the parameter of the trained subnetwork model” in claim 13 is also is adding insignificant extra-solution activity (amounts to necessary data gathering) to the judicial exception, as discussed in MPEP § 2106.05(g).
Merely asserting that a judicial exception is to be carried out on a generic computer (i.e., with the generically-recited “subnetwork model” and 1st devices) cannot meaningfully integrate the judicial exception into a practical application. See MPEP § 2106.05(f).
This limitation does nothing to alter the fundamental nature of the claim as a mental process. This is because the additional limitation merely limits the invention to a narrower abstract idea by further narrowing what the receiving a parameter of the trained subnetwork model includes.
Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. Thus, this new limitation does nothing to alter the analysis of claim 11.
The claim is directed to an abstract idea.
Step 2B Analysis: The claim does not recite additional elements that are sufficient to amount to significantly more than the judicial exception.
Mere instructions to apply the mental process electronically (i.e., with the generically-recited model and the 1st “devices”) do not amount to significantly more than the judicial exception. As noted above, merely asserting that a judicial exception is to be carried out on a generic computer cannot provide significantly more than the judicial exception. See MPEP § 2106.05(f).
Moreover, receiving, communicating, forwarding and storing data are insignificant extra-solution activities that are well-understood, routine, and conventional. See MPEP2106.05(d)(II) (“The courts have recognized the following computer functions as well-understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity. i. Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); … iv. Storing and retrieving information in memory”) (citing OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015)) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network)". Therefore, the recitation of “wherein the receiving a parameter of the trained subnetwork model further comprises: receiving, from the plurality of first devices, a change in the parameter of the trained subnetwork model” are the well-understood, routine, conventional activities of receiving and transmitting data over a network, as discussed in MPEP § 2106.05(d).
Accordingly, at Step 2B, the additional elements do not amount to significantly more than the judicial exception.
The additional element does not provide an inventive concept, and, therefore, the claim is not patent eligible.
Regarding claim 14, this claim is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 14 is directed to method as depending from claim 11, thus the analysis for patent eligibility of claim 11 is incorporated herein.
Step 2A Prong One Analysis: The claim is directed to an abstract idea. In particular, the claim recites mental processes that are concepts performed in the human mind (including an observation, evaluation, judgment, opinion).
The claim recites: wherein the updating the hypernetwork model comprises: updating the hypernetwork model based on an update weight of the parameter of the subnetwork model, wherein the update weight depends on a quantity of subnetwork models having the parameter. - Under its BRI, in light of the specification, the updating limitation encompasses the mental process of updating/modifying a generically-recited hypernetwork model based on received/observed data (a weight value of a parameter) where the weight depends on an observed/determined quantity of subnetwork models having the parameter (i.e., evaluation, judgment, opinion to update/modify a model based on a parameter weight that is in turn based on observation of data – a number/quantity of subnetwork models having the parameter).
That is, other than language reciting generic computer components (i.e., the generically-recited models, and using a generic computer programmed with a generic class of computer algorithm, the models), the above limitation in the context of this claim encompasses updating a parameter based on received/observed weight value/data (i.e., evaluation, judgement, opinion), which are considered mental processes.
Regarding the “hypernetwork model” and “subnetwork model” limitations, no details of models are recited and the models give the indication that one of the models can be obtained/created and updated/modified by hand with pen and paper based on observed and received data/parameters and weights (i.e., evaluation/judgement/opinion). Given a sufficiently small set of parameter values and weights, such updating can be done with pen and paper.
The “hypernetwork model” and “subnetwork model” are recited at a high level of generality and therefore are being interpreted as performing a mental process on a generic computer. See MPEP 2106.04(a)(2) § III.C which states that “a concept that is performed in the human mind and applicant is merely claiming that concept performed 1) on a generic computer, or 2) in a computer environment, or 3) is merely using a computer as a tool to perform the concept” still recite a mental process.
If the claim limitations, under their broadest reasonable interpretations, cover performance of the limitations in the mind but for the recitation of generic computer components (i.e., with the generically-recited models), then they fall within the “Mental Processes” grouping of abstract ideas. Accordingly, claim 14 recites an abstract idea.
Step 2A Prong Two Analysis: The judicial exceptions are not integrated into a practical application.
The claim does not recite any additional elements that integrate the abstract idea into a practical application or provide significantly more than the abstract idea, and thus the claim is subject-matter ineligible.
Step 2B Analysis: The claim does not recite additional elements that are sufficient to amount to significantly more than the judicial exception.
This claim is not patent eligible.
Regarding claim 15, this claim is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 15 is directed to method as depending from claim 11, thus the analysis for patent eligibility of claim 11 is incorporated herein.
Step 2A Prong One Analysis: The claim is directed to an abstract idea. In particular, the claim recites mental processes that are concepts performed in the human mind (including an observation, evaluation, judgment, opinion).
The claim recites: determining, by the second device, a preconfigured parameter of the hypernetwork model for the plurality of first devices to determine respective subnetwork models from the hypernetwork model. - Under its BRI, in light of the specification, the determining limitation encompasses the mental process of determining/identifying, a preconfigured parameter of a generically-recited hypernetwork model in order to determine subnetwork models (i.e., evaluation, judgment, opinion to determine subnetwork models based on an observed parameter).
That is, other than language reciting generic computer components (i.e., the generically-recited models and the 2nd device, and using a generic computer programmed with a generic class of computer algorithm, the models), the above limitation in the context of this claim encompasses determining a parameter in order to determine subnetwork models based on received/observed data-the parameter (i.e., evaluation, judgement, opinion), which are considered mental processes.
Regarding the “hypernetwork model” and “subnetwork models” limitations, no details of models are recited and the models give the indication that one of the models can be obtained/created and updated/modified by hand with pen and paper based on observed and received data/parameters and weights (i.e., evaluation/judgement/opinion). Given a sufficiently small set of parameter values and weights, such updating can be done with pen and paper.
The “hypernetwork model” and “subnetwork models” are recited at a high level of generality and therefore are being interpreted as performing a mental process on a generic computer. See MPEP 2106.04(a)(2) § III.C which states that “a concept that is performed in the human mind and applicant is merely claiming that concept performed 1) on a generic computer, or 2) in a computer environment, or 3) is merely using a computer as a tool to perform the concept” still recite a mental process.
If the claim limitations, under their broadest reasonable interpretations, cover performance of the limitations in the mind but for the recitation of generic computer components (i.e., with the generically-recited models), then they fall within the “Mental Processes” grouping of abstract ideas. Accordingly, claim 15 recites an abstract idea.
Step 2A Prong Two Analysis: The judicial exceptions are not integrated into a practical application.
The claim recites the additional limitation of determining, by the second device, a preconfigured parameter. The above-noted additional element in the claim amounts to recitation of the words "apply it" (or an equivalent) or are mere instructions to implement an abstract idea or other exception on a computer, which does not integrate a judicial exception into a practical application. See MPEP 2106.05(f).
Merely asserting that a judicial exception is to be carried out on a generic computer (i.e., with the generically-recited models and the 2nd “device”) cannot meaningfully integrate the judicial exception into a practical application. See MPEP § 2106.05(f).
The claim does not recite any additional elements that integrate the abstract idea into a practical application or provide significantly more than the abstract idea, and thus the claim is subject-matter ineligible.
Step 2B Analysis: The claim does not recite additional elements that are sufficient to amount to significantly more than the judicial exception.
Mere instructions to apply the mental process electronically (i.e., with the generically-recited models and 2nd “device”) do not amount to significantly more than the judicial exception. As noted above, merely asserting that a judicial exception is to be carried out on a generic computer cannot provide significantly more than the judicial exception. See MPEP § 2106.05(f).
This claim is not patent eligible.
Regarding claim 16, this claim is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 16 is directed to method as depending from claim 15, thus the analysis for patent eligibilities of claim 15 and of base claim 11 are incorporated herein.
Step 2A Prong One Analysis: The claim is directed to an abstract idea. In particular, the claim recites mental processes that are concepts performed in the human mind (including an observation, evaluation, judgment, opinion).
The claim recites: wherein the determining a preconfigured parameter comprises:
determining the preconfigured parameter based on a local parameter determined - Under its BRI, in light of the specification, the determining limitation encompasses the mental process of determining/identifying, a preconfigured parameter based on an observed local parameter (i.e., evaluation, judgment, opinion to determine subnetwork models based on an observed parameter).
Accordingly, claim 16 recites an abstract idea.
Step 2A Prong Two Analysis: The judicial exceptions are not integrated into a practical application.
The claim recites the additional limitation of by locally training the hypernetwork model by the plurality of first devices. - The above-noted additional element in the claim amounts to recitation of the words "apply it" (or an equivalent) or are mere instructions to implement an abstract idea or other exception on a computer, which does not integrate a judicial exception into a practical application. See MPEP 2106.05(f). In particular, “locally training the hypernetwork model by the plurality of first devices” is simply generic training to perform the abstract idea of processing received parameter data to create a generically-recited hypernetwork model by generic 1st devices and amounts to mere instructions to apply the exception (MPEP 2106.05(f)).
Merely asserting that a judicial exception is to be carried out on a generic computer (i.e., with the generically-recited model and the 1st “devices” cannot meaningfully integrate the judicial exception into a practical application. See MPEP § 2106.05(f).
Step 2B Analysis: The claim does not recite additional elements that are sufficient to amount to significantly more than the judicial exception.
As discussed above with respect to the integration of the abstract idea into a practical application, the additional element of:
“locally training the hypernetwork model by the plurality of first devices” is generic training to perform the abstract idea and amounts to no more than mere instructions to apply the exception (MPEP 2106.05(f)).
Mere instructions to apply the mental process electronically (i.e., with the generically-recited model and 1st “devices”) do not amount to significantly more than the judicial exception. As noted above, merely asserting that a judicial exception is to be carried out on a generic computer cannot provide significantly more than the judicial exception. See MPEP § 2106.05(f).
This claim is not patent eligible.
Claim Rejections - 35 USC § 102
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 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.
Claims 1-3, 9, 11-13, 15-18 and 20 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by non-patent literature non-patent literature Wang, Chunnan, et al. ("FL-AGCNS: federated learning framework for automatic graph convolutional network search." arXiv preprint arXiv:2104.04141 (April 2021), hereinafter “Wang”).
With respect to claim 1, Wang discloses the invention as claimed including a method for generating a neural network model3 (see, e.g., Abstract, “Graph Convolutional Network (GCN) architectures. … apply to the Federated Learning (FL) … we propose FL-AGCNS, an efficient GCN NAS algorithm suitable for FL scenarios.” and page 2, § 1, “The combination of GCN NAS and FL strengthens the practicality of the GCN NAS method … We propose to use GCN SuperNet to reduces the search cost of the GCN NAS method” and page 3, § 3, lines 1-3 ("we design the FL-AGCNS algorithm to deal with the HFL based GCN NAS problem. We apply GCN SuperNet to achieve fast evaluation of GCN architectures" [i.e., a method for generating a GCN/neural network model]), comprising:
sending, by a first device, an indication about a structure of a subnetwork model to a second device (see, e.g., FIG. 1 – showing that "Each client is responsible for providing its preferred architectures … and the controller aims to guide clients to search for the optimal GCN architecture” and “Send[ing] Top PGi" by the “Clients” in the “Federated Evolutionary Optimization”, and page 5, Algorithm 1, step 6 of the client procedure ("Send P'Gi … to Controller" [i.e., sending, by a 1st client device an indication PGi about an architecture/structure of a subnetwork model to a 2nd device/controller] and step 5 “P’Gi [Wingdings font/0xDF] TOP” [i.e., PGi corresponds to a population of graph convolutional network/GCN models, and the subnetwork model is one of these models. The representation of this model in Top PGi is the indication]), wherein the subnetwork model is determined by adjusting a structure of a hypernetwork model (see, e.g., page 3, Fig. 1 – depicting, in the dashed box on the right, "SuperNet evolution" at the Clients' side as part of “Federated Evolutionary Optimization” and prior to obtaining the "new PGi" from which the top PGi is/are then sent to the “Controller” and page 4, Fig. 2 showing “GCN SuperNet Structure” and § 3, lines 7-9, "we optimize SuperNet weights and population successively in each training step") [i.e., determine subnetwork model/PGi by evolving/adjusting SuperNet/hypernetwork model structure]);
receiving, by the first device, a parameter of the subnetwork model from the second device, wherein the parameter of the subnetwork model is determined by the second device based on the indication and the hypernetwork model (see, e.g., pages 5-6, Algorithm 1, step 6 at the controller (where the population P is updated while keeping P'Gi as part of it) in combination with step 7 of Algorithm 2, “Controller: send P to N Clients.”, also shown in the left dashed box of Fig. 1 as "Receive P" at the “Clients” side [i.e., Algorithm 2 is iterative, involving iterations of the overall operations shown in the right dashed box and the left dashed box of Fig. 1. By receiving the new P, the client(s) is/are receiving subnetwork model P'Gi and its parameters]);
training, by the first device, the subnetwork model based on the received parameter of the subnetwork model (see, e.g., page 4, col, 2, lines 12-28, “Given a ϵ P, the gradient of its architecture parameters Wa can be calculated as follows ... train Gi … train Gj … each client calculates dWPGi separately") [i.e., training by the 1st device/controller based on the received parameters of subnetwork model PGi]); and
sending, by the first device, a parameter of the trained subnetwork model to the second device for the second device to update the hypernetwork model (see, e.g., page 4, col, 2, lines 27-28, "each client calculates dWPGi separately, then sends the encrypted gradient information to the controller" and page 5, col. 1, lines 1-3, "The controller aggregates ... J and sends it to all clients.” [i.e., sending, by the 1st device/controller, a parameter and gradient information of the trained subnetwork model PGi to the client/2nd device]).
With respect to claim 11, Wang discloses the invention as claimed including a method for generating a neural network model4 (see, e.g., Abstract, “Graph Convolutional Network (GCN) architectures. … apply to the Federated Learning (FL) … we propose FL-AGCNS, an efficient GCN NAS algorithm suitable for FL scenarios.” and page 2, § 1, “The combination of GCN NAS and FL strengthens the practicality of the GCN NAS method … We propose to use GCN SuperNet to reduces the search cost of the GCN NAS method” and page 3, § 3, lines 1-3 ("we design the FL-AGCNS algorithm to deal with the HFL based GCN NAS problem. We apply GCN SuperNet to achieve fast evaluation of GCN architectures" [i.e., a method for generating a GCN/neural network model]), comprising:
receiving, by a second device, an indication about a structure of a subnetwork model from a plurality of first devices (see, e.g., FIG. 1, showing that "Each client is responsible for providing its preferred architectures … and the controller aims to guide clients to search for the optimal GCN architecture” and depicting "Receive Top PGi from N clients" [i.e., the 1st devices] by the controller/2nd device in the “Federated Evolutionary Optimization” and page 5, Algorithm 1, step 4 of the controller procedure to “Receive P’Gi” from clients/1st devices [i.e., PGi corresponds to a population of graph convolutional network/GCN models, and the subnetwork model is one of these models. The representation of this model in Top PGi is the received indication]), wherein the subnetwork model is determined by adjusting a structure of a hypernetwork model (see, e.g., FIG. 1 -- the right dashed box depicting "SuperNet evolution" at the “Clients” side during “Federated Evolutionary Optimization” and prior to obtaining the "new PGi" from which the top PGi which are then sent to the controller/2nd device and page 4, FIG. 2 showing “GCN SuperNet Structure” and § 3, lines 7-9, "we optimize SuperNet weights and population successively in each training step") [i.e., determine subnetwork model/PGi by evolving/adjusting SuperNet/hypernetwork model structure]);
determining, by the second device, a parameter of the subnetwork model based on the indication and the hypernetwork model (see, e.g., pages 5-6, Algorithm 1, step 6 at the controller (where the population P is updated while keeping P'Gi as part of it) in combination with step 7 of Algorithm 2, wherein the controller/2nd device determines and sends P to the clients/1st devices, also shown in the left dashed box of Fig. 1 as "Receive P" at the “Clients”/1st devices side [i.e., Algorithm 2 is iterative, involving iterations of the overall operations shown in the right dashed box and the left dashed box of Fig. 1. By receiving the new P, the client(s) is/are receiving P'Gi, and by extension, its parameters]);
sending, by the second device, the parameter of the subnetwork model to the plurality of first devices for the plurality of first devices to separately train the subnetwork model (see, e.g., pages 5-6, Algorithm 1, step 6 at the controller (where the population P is updated while keeping P'Gi as part of it) in combination with step 7 of Algorithm 2, wherein the controller/2nd device sends P to the clients/1st devices, also shown in the left dashed box of Fig. 1 as "Receive P" at the “Clients”/1st devices side [i.e., Algorithm 2 is iterative, involving iterations of the overall operations shown in the right dashed box and the left dashed box of Fig. 1. By receiving the new P, the client(s) is/are receiving P'Gi, and by extension, its parameters] and page 4, col, 2, lines 12-28, “Given a ϵ P, the gradient of its architecture parameters Wa can be calculated as follows ... train Gi … train Gj … each client calculates dWPGi separately” [i.e., to separately train the subnetwork model by the 1st devices/controller based on the received parameters of subnetwork model PGi]);
receiving, by the second device, a parameter of the trained subnetwork model from the plurality of first devices (see, e.g., page 4, col, 2, lines 27-28, "each client calculates dWPGi separately, then sends the encrypted gradient information to the controller" [i.e., receiving, by the 2nd device/controller, a parameter and gradient information of the trained subnetwork model PGi from the clients/1st devices]); and
updating, by the second device, the hypernetwork model by using the received parameter (see, e.g., page 5, col. 1, lines 1-3, “The controller aggregates ... J and sends it to all clients.” [i.e., updating, by the controller/2nd device, the supernet/hypernetwork model by using the parameter and gradient information received from the clients/1st devices]).
With respect to independent claim 17, claim 17 is substantially similar to claim 1 and therefore is rejected on the same ground as claim 1, discussed above. In particular, claim 17 is an apparatus claim that performs operations that correspond to the method steps of claim 1.
In addition, Wang further discloses an apparatus for generating a neural network model5, comprising: a sending unit configured to send, … a receiving unit, configured to receive … ; and a training unit6, configured to train (see, e.g., Abstract, “Graph Convolutional Network (GCN) architectures. … apply to the Federated Learning (FL) … we propose FL-AGCNS, an efficient GCN NAS algorithm suitable for FL scenarios. FL-AGCNS designs a federated evolutionary optimization strategy to enable distributed agents to cooperatively design powerful GCN models while keeping personal information on local devices.”, page 2, § 1, left col., lines 1-5, “we … propose FL-AGCNS, an efficient GCN NAS algorithm, that enables distributed agents to cooperatively design powerful GCN models while keeping personal information on local devices”, page 4, col, 2, lines 12-28, “Given a ϵ P, the gradient of its architecture parameters Wa can be calculated as follows ... train Gi … train Gj … each client calculates dWPGi separately" and page 7, footnote 4, “we simulate N = 8 clients instead in the Physics dataset, due to GPU memory constraints”, page 3, FIG. 1 depicting Controller/1st device and “Clients”/2nd devices with units/modules to “Send” and “Receive” indications of models and their parameters, page 4 § 3.2, right col. and pages 5-6, Algorithms 1 and 2 with steps for the “Controller”/1st device and “Client”/2nd device to “Send” and “Receive” indications of models and their parameters [i.e., a device/apparatus with a GPU and memory for generating a GCN/neural network model, and algorithms/units for the sending/receiving and training]).
Regarding claims 2 and 18, as discussed above, Wang discloses the method of claim 1 and the apparatus of claim 17.
Wang further discloses obtaining, by the first device, a preconfigured parameter of the hypernetwork model (see, e.g., page 2, left col. “optimize parameters of GCN SuperNet, and efficiently evaluate various GCN architectures by sharing corresponding parameters in SuperNet” and page 3 right col. “In FL-AGCNS we use (2 x L+2) parameters (L is the number of GCN layers)” and FIG. 1 – depicting “Clients” side left dashed box that “Receive P” as part of “Federated SuperNet Optimization” [i.e., receiving/obtaining, by the 1st device/client, a preconfigured parameter of the SuperNet/hypernetwork model P. By receiving the new P, the client/1st device is obtaining/receiving SuperNet/model and its parameters – including a preconfigured parameter]); and
determining, by the first device, the structure of the subnetwork model based on the preconfigured parameter and by adjusting the structure of the hypernetwork model (see, e.g., Algorithm 2, step 3; page 4, col. 1, lines 10-11 "GCN superNet should be stored in each client"; FIG. 2 – depicting “Code Representation and GCN SuperNet Structure” and FIG. 1, depicting the "SuperNet evolution" at the “Clients” side during the “Federated Evolutionary Optimization” and prior to obtaining the "new PGi" from which the top PGi are then sent to the controller/1st device that then determines the structure of the subnetwork model based on the preconfigured parameter and evolving/adjusting the structure of the SuperNet/hypernetwork model).
Regarding claim 3, as discussed above, Wang discloses the method of claim 2.
Wang further discloses wherein the obtaining a preconfigured parameter of the hypernetwork model comprises:
locally training, by the first device, the hypernetwork model to determine a local parameter of the hypernetwork model (see, e.g., page 2, left col. “optimize parameters of GCN SuperNet, and efficiently evaluate various GCN architectures by sharing corresponding parameters in SuperNet” and page 4, col, 2, lines 12-28, “Given a ϵ P, the gradient of its architecture parameters Wa can be calculated as follows ... train Gi … train Gj … each client calculates dWPGi separately" [i.e., locally training, by the client/1st device, the population P of architectures from the hypernetwork model at the client/1st device to determine a local parameter of the SuperNet/hypernetwork model]);
sending, by the first device, the local parameter to the second device (see, e.g., page 5, col. 1, line 3, "In the federated SuperNet optimization ... sends it to all clients” and page 6, Algorithm 2, step 10-11, “Client i (i=1,...,N): Send EN[dWPGi] to Controller.” [i.e., sending the obtained parameters to the controller/2nd device]); and
receiving, by the first device, the preconfigured parameter from the second device, wherein the preconfigured parameter is determined by the second device based on at least the local parameter received from the first device (see, e.g., page 4, col. 1, lines 10-11 "GCN superNet should be stored in each client" and page 6, Algorithm 2, step 11, “Controller: Receive gradient information from N Clients” [i.e., subsequently receiving, by the 1st device/client, the updated weights and gradient information of the Hypernetwork model from the controller/2nd device]).
Regarding claims 9 and 12, as discussed above, Wang discloses the method of claim 1 and the method of claim 11.
Wang further discloses wherein the indication is in a form of a mask indicating whether the subnetwork model has a corresponding parameter of the hypernetwork model (see, e.g., page 4, col. 1, lines 11-15, “Let Wa = W ʘ ca be the corresponding parameters of a ϵ A, where ʘ is the mask operation that keeps parameters of the complete SuperNet only for positions corresponding to the operations applied in code ca.” and page 4, col, 2, lines 12-28, “Given a ϵ P, the gradient of its architecture parameters Wa can be calculated” [i.e., using a mask ʘ as indication in the mask-based approach, the indication is in the form of mask ʘ indicating if the subnetwork has a corresponding parameter in the SuperNet/hypernetwork model]).
Regarding claim 13, as discussed above, Wang discloses the method of claim 11.
Wang further discloses wherein the receiving a parameter of the trained subnetwork model further comprises:
receiving, from the plurality of first devices, a change in the parameter of the trained subnetwork model (see, e.g., pages 4-5, "In the federated SuperNet optimization each client calculates dWPGi separately, then sends the … gradient information to the controller. The controller aggregates the local information" and page 6, Algorithm 2, step 10, “Client i (i=1,...,N): Send EN[dWPGi] to Controller.” [i.e., receiving from the 1st devices/clients, a change in the parameter and gradient information of the trained subnetwork model]).
Regarding claim 15, as discussed above, Wang discloses the method of claim 11.
Wang further discloses determining, by the second device, a preconfigured parameter of the hypernetwork model for the plurality of first devices to determine respective subnetwork models from the hypernetwork model (see, e.g., page 2, left col. “optimize parameters of GCN SuperNet, and efficiently evaluate various GCN architectures by sharing corresponding parameters in SuperNet” and page 3 right col. “In FL-AGCNS we use (2 x L+2) parameters (L is the number of GCN layers)” and FIG. 1 – depicting “Clients” side left dashed box that “Receive P” as part of “Federated SuperNet Optimization” [i.e., determining, by the 2nd device/controller, a preconfigured parameter of the SuperNet/hypernetwork model P for the 1st devices/clients, which receive P SuperNet/model and its parameters – including a preconfigured parameter to determine subnetworks from the SuperNet] and Algorithm 2, step 3 and page 4, col. 1, lines 10-11 "GCN superNet should be stored in each client" and FIG. 1 - depicting the “Controller” side during the “Federated Evolutionary Optimization” and prior to obtaining the "new PGi" from which the top PGi are then received by the controller/2nd device that then determines the structure of the subnetwork models based on the preconfigured parameter).
With respect to independent claim 20, claim 20 is substantially similar to claim 11 and therefore is rejected on the same ground as claim 11, discussed above. In particular, claim 20 is an apparatus claim that performs operations that correspond to the method steps of claim 1.
In addition, Wang further discloses an apparatus for generating a neural network model7, comprising: a receiving unit configured to receive … ; a unit for determining a parameter … ; a sending unit configured to send, … a hypernetwork update unit8, … configured to update (see, e.g., Abstract, “Graph Convolutional Network (GCN) architectures. … apply to the Federated Learning (FL) … we propose FL-AGCNS, an efficient GCN NAS algorithm suitable for FL scenarios. FL-AGCNS designs a federated evolutionary optimization strategy to enable distributed agents to cooperatively design powerful GCN models while keeping personal information on local devices.”, page 2, § 1, left col., lines 1-5, “we … propose FL-AGCNS, an efficient GCN NAS algorithm, that enables distributed agents to cooperatively design powerful GCN models while keeping personal information on local devices”, page 4, col, 2, lines 12-28, “Given a ϵ P, the gradient of its architecture parameters Wa can be calculated as follows ... train Gi … train Gj … each client calculates dWPGi separately" and page 7, footnote 4, “we simulate N = 8 clients instead in the Physics dataset, due to GPU memory constraints”, page 3, FIG. 1 depicting Controller/1st device and “Clients”/2nd devices with units/modules to “Send” and “Receive” indications of models and their parameters, page 4 § 3.2, right col. and pages 5-6, Algorithms 1 and 2 with steps for the “Controller”/1st device and “Client”/2nd device to “Send” and “Receive” indications of models and their parameters [i.e., a device/apparatus with a GPU and memory for generating a GCN/neural network model, and algorithms/units for the sending/receiving and training/updating the supernet/hypernetwork]).
Regarding claim 16, as discussed above, Wang discloses the method of claim 15.
Wang further discloses wherein the determining a preconfigured parameter comprises:
determining the preconfigured parameter based on a local parameter determined by locally training the hypernetwork model by the plurality of first devices (see, e.g., page 2, left col. “optimize parameters of GCN SuperNet, and efficiently evaluate various GCN architectures by sharing corresponding parameters in SuperNet” and page 4, col, 2, lines 12-28, “Given a ϵ P, the gradient of its architecture parameters Wa can be calculated as follows ... train Gi … train Gj … each client calculates dWPGi separately" [i.e., locally training, by the clients/1st devices, the population P of architectures from the hypernetwork model at the client/1st devices to determine a preconfigured parameter of the SuperNet/hypernetwork model]).
Conclusion
The prior art made of record, listed on form PTO-892, and not relied upon, is considered pertinent to applicant's disclosure.
The references listed on form PTO-892 are all generally related to using subnetworks and supernetworks/hypernetworks (i.e., global networks) for federated learning/joint training techniques, methods and systems.
For example, non-patent literature Ehret, Benjamin, et al. "Continual learning in recurrent neural networks." arXiv preprint arXiv:2006.12109 v3 (Mar 2021) hereinafter “Ehret,”) discloses “We performed joint training by assembling a mini-batch of size B using samples equally distributed across all K datasets” and “A hypernetwork h(e,ϴ) produces the weights ψ of a recurrent main network … Here, we illustrate a hypernetwork-based CL [continual learning] approach … Masking (or context-dependent gating, … applies a binary random mask per task for all hidden units of a multi-head network, and can be seen as a simple method for selecting a different subnetwork per task… this method can be combined with other CL methods such as SI (Masking+SI).” (see, pages 1 and 4, FIG. 1).
The examiner requests, in response to this office action, support be shown for language added to any original claims on amendment and any new claims. That is, indicate support for newly added claim language by specifically pointing to page(s) and line no(s) in the specification and/or drawing figure(s). This will assist the examiner in prosecuting the application.
When responding to this office action, Applicant is advised to clearly point out the patentable novelty which he or she thinks the claims present, in view of the state of the art disclosed by the reference cited or the objections made. He or she must also show how the amendments avoid such references or objections See 37 CFR 1.111 (c).
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/RANDALL K. BALDWIN/Primary Examiner, Art Unit 2125
1 References to the specification herein are to the specification as amended in the 1/09/2024 preliminary amendment.
2 As indicated in the objections to these claims above, it appears the recitations of “a hypernetwork model” should recite “the hypernetwork model” to unambiguously refer to the previously-introduced “hypernetwork model”
3 “A method for generating a neural network model” is intended use language with no patentable weight because aside from these recitations, no “neural network model” let alone any operations or steps “for generating a neural network model” are recited elsewhere in the claim or any of its dependent claims. Instead, the method steps of claim 1 only refer to a hypernetwork model, a subnetwork model and a training of subnetwork model without referring to a "neural network”.
4 “A method for generating a neural network model” is intended use language with no patentable weight because aside from these recitations, no “neural network model” let alone any operations or steps “for generating a neural network model” are recited elsewhere in the claim or any of its dependent claims. Instead, the method steps of claim 1 only refer to a hypernetwork model, a subnetwork model and a training of subnetwork model without referring to a "neural network”.
5 “An apparatus for generating a neural network model” is intended use language with no patentable weight because aside from these recitations, no “neural network model” let alone any operations or steps “for generating a neural network model” are recited elsewhere in the claim or any of its dependent claims. Instead, the operations of claim 17 only refer to a hypernetwork model, a subnetwork model and a training of subnetwork model without referring to a "neural network”.
6 As indicated above in the section 112(b) rejection of this claim, the above-listed units have been interpreted as any combination of software (i.e., a set of instructions, code, one or more algorithms, functions or software modules) and/or hardware (i.e., circuitry and/or hardware logic components/modules) capable of performing the claimed functions.
7 “An apparatus for generating a neural network model” is intended use language with no patentable weight because aside from these recitations, no “neural network model” let alone any operations or steps “for generating a neural network model” are recited elsewhere in the claim. Instead, the operations of claim 20 only refer to a hypernetwork model, a subnetwork model and a training of subnetwork model without referring to a "neural network”.
8 As indicated above in the section 112(b) rejection of this claim, the above-listed units have been interpreted as any combination of software (i.e., a set of instructions, code, one or more algorithms, functions or software modules) and/or hardware (i.e., circuitry and/or hardware logic components/modules) capable of performing the claimed functions.
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