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. 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 following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph: 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) or pre-AIA 35 U.S.C. 112, sixth paragraph, 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) or pre-AIA 35 U.S.C. 112, sixth paragraph: (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) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, 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) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, 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) or pre-AIA 35 U.S.C. 112, sixth paragraph, 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) or pre-AIA 35 U.S.C. 112, sixth paragraph, 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) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses 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 limitation(s) is/are: a transmission unit , with corresponding structure found in [0008] ; a signal generation unit , with corresponding structure found in [0 036] ; a receiving unit , with corresponding structure found in [ 0036-0037 ] and an evaluation unit , with corresponding structure found in [0008] [0014] [0043] ; note: the specification does not provide adequate structure defining the “machine learning algorithm” and its function of detecting the boundary layer on the basis of the received signals. Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof. If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. Claim Objections Claim 18 is objected to because of the following informalities: With respect to claim 18, lines 3-4, “the time-domain reflectometry (TDR) method” lacks proper antecedent basis. Appropriate correction is required. Claim Rejections - 35 USC § 112 The following is a quotation of 35 U.S.C. 112(b): (b ) CONCLUSION.— The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention. The following is a quotation of 35 U.S.C. 112 (pre-AIA), second paragraph: The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the appl icant regards as his invention. Claims 11-20 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention. With respect to both claim 11 and 19, the claimed “evaluation unit in which a machine learning algorithm is formed in order to detect the boundary layer on the basis of the received signal” is unclear. When turning to applicant’s disclosure, the machine learning algorithm is generically disclosed, specifically para. [0022] which states “ [ i ] n this case, in particular, supervised learning is implemented as an algorithmic approach. In this connection, the specific form in which the machine learning algorithm is implemented is not firmly prescribed within the scope of the invention . For example, the machine learning algorithm can be implemented in the form of “decision trees,” a “support vector machine ,” “naive Bayes classifiers,” or “k-nearest neighbor.” However, the boundary layer can be detected particularly effectively if the machine learning algorithm is designed on the basis of a non-symbolic approach, such as an artificial neural network, in particular in the form of a deep learning method. Machine learning algorithms are described in more detail, for example, in “ Introduction to Artificial Intelligence ” ”. The examiner is unsure of the metes and bounds of the claimed machine learning algorithm and its use by the evaluation unit. Applicant specification merely details examples and not what or how they tailor these generical ML algorithms to their specific use. Clarification is required. The following is a quotation of the first paragraph of 35 U.S.C. 112(a): (a) IN GENERAL.— The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention. The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112: The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention. Claims 11-20 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention. As described above, the disclosure does not provide adequate structure to perform the claimed function of a machine learning algorithm is formed in order to detect the boundary layer of the basis of the received signal . The specification does not demonstrate that applicant has made an invention that achieves the claimed function because the invention is not described with sufficient detail such that one of ordinary skill in the art can reasonably conclude that the inventor had possession of the claimed invention. 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. Claim(s) 11 , 12 and 14- 20 is/are rejected under 35 U.S.C. 102 (a)(1) as being anticipated by Griessbaum et al. ( 2012/0299768 ) . With respect to claim 1 1 , Griessbaum et al. teaches a measuring system (Fig. 1) for detecting a boundary layer (105; Fig. 1) of a product (106) in a container (109) , comprising: a radar-based measuring device (101) , including: a transmission unit (104) via which high-frequency signals (103) can be transmitted in a direction (shown in Fig. 1) of the product (106) and, after reflection at the product surface ( a surface defined by the boundary layer 105) , can be received as received signals [0047-0048] ; a signal generation unit (102) that is designed to generate the high-frequency signal to be transmitted [0046] ; and a receiving unit (i.e. a portion of 102) that is designed to record the received signal (as reflected back to 102; [0048]) ; and an evaluation unit ( 702; Fig. 7 ) in which a machine learning algorithm (found in 7027; [0084] , [0086] and [0098-0103] ) is formed in order to detect the boundary layer (105) on the basis of the received signal (as the self-learning unit of the evaluation unit determines the length of a dome shaft of a container, thereby defining the boundary layer; abstract) . The method steps of claim 19 are performed during the operation of the rejected structure of claim 11. With respect to claim 1 2, Griessbaum et al. teaches the measuring system wherein the evaluation unit (702) is designed, via the machine learning algorithm (as disclosed in [0084], [0086] and [0098-0103] ) , to determine: a vertical position (defined by d L ) of the boundary layer (105) with respect to a height (d B ) above a bottom of the container (as seen in Fig. 5). With respect to claim 1 4 , Griessbaum et al. teaches the measuring system wherein the machine learning algorithm (i.e. self-learning device) is designed as an artificial neural network (as self-learning devices are artificial neural networks designed to adjust parameters based on new information; therefore, the teaches of Griessbaum read on the claimed limitation) . With respect to claim 1 5 , Griessbaum et al. teaches the measuring system wherein the evaluation unit (702) is designed to determine the fill level of the product [0012] in the container (seen in Fig. 5) on the basis of the received signal (from 104) . With respect to claim 1 6 , Griessbaum et al. teaches the measuring system wherein the evaluation unit (702) is designed as an integral component of the measuring device (as seen in Fig. 5) . With respect to claim 1 7 , Griessbaum et al. teaches the measuring system wherein the evaluation unit (702) is designed as a component of a higher-level network (i.e. insofar as how “a higher-level network” is structurally recited, Fig. 7 shows a higher-level network in the sense that 702 is a network of modules 7021-7027). With respect to claim 1 8 , Griessbaum et al. teaches the measuring system wherein the transmission unit (104) is designed as a measuring sensor (as seen in Fig. 5) extending into the container (as seen in the figure) , and wherein the signal generation unit (102) is designed to generate the high-frequency signal to be transmitted according to the time-domain reflectometry method (as para. [0045-0049] describes the electromagnetic signal propagation, reflection, and transit time involves in TDR method) . With respect to claim 20, Griessbaum et al. teaches the method wherein the machine learning algorithm is learned by means of experimentally obtained and/or simulation-generated received signals (as Griessbaum et al. teaches using a self-learning system to detect the boundary layer and height information, as read in the abstract; although not explicitly states, self-learning systems utilize training through experimentation and simulations to improve their performance and operation; therefore, the teaching of using a self-learning system reads on the claimed limitation). Claim Rejections - 35 USC § 103 In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis ( i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status. The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action: A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made. Claim (s) 13 is/are rejected under 35 U.S.C. 103 as being unpatentable over Griessbaum et al. (2012/0299768) in view of Armstrong et al. (2020/0193620) . With respect to claim 13, Griessbaum et al. teaches all that is claimed in the above rejection of claim 11, but remains silent regarding the evaluation unit is designed to detect, via the machine learning algorithm, the mass or volume fraction of the product in the boundary layer, in that the machine learning algorithm determines along the measuring sensor a distribution of the attenuation coefficient and/or of the dielectric constant in the container . Armstrong et al. teaches a similar unit that is designed to detect, via a machine learning algorithm (as Armstrong et al. teaches using a machine learning algorithm trained with occupied fractions of a storage volume of a contained) , the v olume fraction of a product in a boundary layer (of that container using the trained machine learning algorithm; [0032]) , in that the machine learning algorithm (as trained) determines along a measuring sensor (i.e. content sensor; Fig. 3A) a distribution of the attenuation coefficient (as Armstrong teaches using a radar sensor, which utilizes an attenuation constant of a medium in which a signal passes through the volume of material being sensed ; [0023] ) . It would have been obvious to one of ordinary skill in the art before the effective filing of the instant invention to modify the evaluation unit to include the algorithmic process and training data based on an attenuation constant of the medium to determin e the volume fraction of the product within the container, as taught by Armstrong et al, because such a modification increases the versatility of Griessbaum et al. by providing a system that allows a user to know how much product is in the container without having to look inside . Conclusion The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Fay et al. (5,789,676) which teaches sensing a liquid-solid media in a container using an emitted signal into the container. 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