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 . In the event the determination of the status of the application as subject to 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.
Status of Claims
Claims 1-16 are currently pending and are being hereby examined herein.
Joint Inventors
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Effective Filing Date
Acknowledgment is made of applicant’s claim for foreign priority under 35 U.S.C. 119 (a)-(d). The certified copy of DE10 2022 132 747.7 filed on 9 December 2022 was received on 6 June 2025. Furthermore, this application is the national stage entry of PCT/EP2023/084504 filed 6 December 2023.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 6 June 2025 has been considered by the examiner.
Claim Interpretation
The Examiner notes that method claims with contingent limitations (e.g., “if”) are currently pending. The Examiner notes “The broadest reasonable interpretation of a method (or process) claim having contingent limitations requires only those steps that must be performed and does not include steps that are not required to be performed because the condition(s) precedent are not met” (see MPEP 2111.04). If Applicant intends for the claim scope to require each step to be performed in the broadest reasonable interpretation, the Examiner recommends Applicant amends the claim language from contingent language to “in response to” when supported by the original disclosure (Applicant should ensure all amendments have support in the original disclosure and should make any necessary grammatical adjustments).
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 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 data processing device which is configured to” (Claim 14)
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f), it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
“Data processing device” is a generic computer, or equivalents thereof (see at least page 14 of the specification).
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f), applicant may: (1) amend the claim limitation(s) to avoid it/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 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).
Claim Objections
The claims are objected to because of the following informalities:
Claim 1 / Claim 2 / Claim 4: “the target value” should be “the specified target value”.
Claim 4: “which comprises” should be changed to “the metric [[which]] comprises” or “the natural exponential function [[which]] comprises”.
Appropriate correction is required.
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.
Claim 14 is rejected under 35 U.S.C. 112(a) because the claim purports to invoke 35 U.S.C. 112(f), but fails to recite a combination of elements as required by that statutory provision and thus cannot rely on the specification to provide the structure, material or acts to support the claimed function. As such, the claim recites a function that has no limits and covers every conceivable means for achieving the stated function, while the specification discloses at most only those means known to the inventor. Accordingly, the disclosure is not commensurate with the scope of the claim. Appropriate corrections are required.
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.
Claim 1-16 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. Claim 1 recites “a metric” twice (“providing hyperparameters for the evaluation model, wherein the hyperparameters are configured to be adjusted on the basis of a metric” and “providing a metric and evaluating the metric”). Therefore, through-out the claims when “the metric” is referenced one of ordinary skill in the art would not know which “a metric” is referenced, and the claims are indefinite. For the purposes of compact prosecution, the examiner will assume any reference to “the metric” could refer to either introduction of “a metric” and the Examiner will assume the two introductions of “a metric” may be the same as each other or different from one another. Claims 2-16 refer to Claim 1 (either directly or indirectly) and are similarly rejected. Appropriate corrections, including updating / clarifying antecedent basis for all claims, are required.
Claim 10-16 are further 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. Claim 10 is a separate method than Claim 1 (Claim 1 is “A method for producing an evaluation model…”, Claim 10 is “A method for training an evaluation model…”); however, Claim 10 only refers to Claim 1 in the preamble, so one of ordinary skill in the art would find it indefinite which portions of Claim 1 are limiting on Claim 10 / the dependency of Claim 10 upon Claim 1. Furthermore, the language of Claim 10 does not appear to take antecedent basis from Claim 1 (e.g., each claim refers to “an evaluation model”), one of ordinary skill in the art would expect Claim 10 to have antecedent basis from Claim 1 if Claim 10 was in fact dependent on Claim 1. For the purposes of compact prosecution, the Examiner will assume each limitation of Claim 1 is required for Claim 10 and Claim 10 is dependent upon Claim 1. (Examiner notes this interpretation is the reason there is no rejection under 35 U.S.C. 112(d) at this time, if a different interpretation is necessitated by amendment in the future, then a rejection under 35 U.S.C. 112(d) may be necessary at that time). Claims 11-16 refer to Claim 10 (either directly or indirectly) and are similarly rejected. Appropriate corrections, including updating / clarifying antecedent basis for all rejected claims, are required. Claims 11-16 are further 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. Claim 11 references Claim 10. However, Claim 11 is a separate method than Claims 1 and 10 (Claim 11 is “A method for the automated detection of a seat occupancy state…”, Claim 1 is “A method for producing an evaluation model…”, Claim 10 is “A method for training an evaluation model…”). The language of Claim 11 does not appear to take antecedent basis from Claim 10 (e.g., each claim refers to “an associated radar point cloud”, “a seat occupancy state of a seating arrangement”, “an evaluation model”), one of ordinary skill in the art would expect Claim 11 to have antecedent basis from Claim 10 if Claim 11 was in fact dependent on Claim 10. Therefore, one of ordinary skill in the art would not be able to determine the metes and bounds of Claim 11 and the claim is indefinite. For the purposes of compact prosecution, the Examiner will assume each limitation of Claim 10 is required for Claim 11 and Claim 11 is dependent upon Claim 10. (Examiner notes this interpretation is the reason there is no rejection under 35 U.S.C. 112(d) at this time, if a different interpretation is necessitated by amendment in the future, then a rejection under 35 U.S.C. 112(d) may be necessary at that time). Claims 12-16 refer to Claim 11 (either directly or indirectly) and are similarly rejected. Appropriate corrections, including updating / clarifying antecedent basis for all rejected claims, are required.
Claim 16 is further 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. Claim 16 references Claim 14. However, the language of Claim 16 does not appear to take antecedent basis from Claim 14 (e.g., each claim refers to “a seating arrangement having at least one seat” and “a seat occupancy state”), one of ordinary skill in the art would expect Claim 16 to have antecedent basis from Claim 14 if Claim 16 was in fact dependent on Claim 14. Therefore, one of ordinary skill in the art would not be able to determine the metes and bounds of Claim 16 and the claim is indefinite. For the purposes of compact prosecution, the Examiner will assume each limitation of Claim 14 is required for Claim 16 and Claim 16 is dependent upon Claim 14. (Examiner notes this interpretation is the reason there is no rejection under 35 U.S.C. 112(d) at this time, if a different interpretation is necessitated by amendment in the future, then a rejection under 35 U.S.C. 112(d) may be necessary at that time). Appropriate corrections, including updating / clarifying antecedent basis for Claim 16, are required.
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.
Claim 15 is rejected under 35 U.S.C 101 because the claimed invention is directed to non-statutory subject matter. The claim does not fall within at least one of the four categories of patent eligible subject matter because the claim is directed to a computer program and therefore software per se (see MPEP 2106.03).
Claims 1-16 are rejected under 35 U.S.C 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1: Claims 1-13 are directed to a method. Claims 14 and 16 are directed to a system. Therefore, Claims 1-14 and 16 are directed to a statutory category. Claim 15 is not directed to patent eligible subject matter (see above); however, Claim 15 is evaluated below for the purposes of compact prosecution.
Step 2A Prong 1: the claimed invention is directed to abstract ideas. The abstract ideas, specifically mathematical processes, in Claim 1 (the independent claim) are as follows:
A method for producing an evaluation model for automated detection of a seat occupancy state of a seat arrangement having at least one seat
providing parameters which are assigned to at least one of a plurality of specified possible seat occupancy states of the seat arrangement
providing hyperparameters for the evaluation model, wherein the hyperparameters are configured to be adjusted on the basis of a metric
determining a detection accuracy, wherein the detection accuracy indicates a discrepancy between the seat occupancy state assigned to the parameters and an evaluation result supplied by the evaluation model with the provided hyperparameters
providing a metric and evaluating the metric, wherein the metric takes into account a difference between the detection accuracy and a specified target value in order to output a value for a determined detection accuracy, wherein the target value indicates a detection accuracy at which the metric reaches an optimum
adjusting the hyperparameters, wherein the metric is optimized by means of an optimization method for this purpose
producing the evaluation model with the adjusted hyperparameters for training with training data for a method for the automated detection of a seat occupancy state of a seat arrangement having at least one seat
Producing a model, as claimed, is a mathematical process. Furthermore, the dependent claims recite additional abstract ideas. (Claims 2-9 are directed solely to abstract ideas).
The additional elements in Claims 1-16 are as follows (the examiner notes contingent limitations, such as those in Claims 12 and 13 are not evaluated, because under the broadest reasonable interpretation of a method claim they need not occur, and in that case would not be additional elements):
A method for training an evaluation model produced in accordance with the method as claimed in claim 1 for the automated detection of a seat occupancy state of a seat arrangement having at least one seat (Claim 10)
capturing measured data which represents an associated radar point cloud, wherein the radar point cloud is or has been obtained on the basis of radar scanning of at least sections of a spatial region encompassing the seat arrangement, and is assigned to one of a plurality of predefined possible seat occupancy states of the seat arrangement (Claim 10)
producing training data from the measured data, wherein the training data is made available to the evaluation model as input data in order to obtain as its output an evaluation result which is assigned to the seat occupancy state of the seat arrangement (Claim 10)
capturing measurement data which represents an associated radar point cloud, wherein each radar point cloud is or has been obtained on the basis of radar scanning of at least sections of a spatial region encompassing the seat arrangement (Claim 11)
wherein the issuing of the information comprises activating a signal source as a function of the information in order to cause the signal source to output a defined signal as a function of the activation (Claim 12)
detecting a seatbelt fastening state of at least one seat in the seating arrangement or receiving seatbelt information characteristic of this seatbelt fastening state (Claim 13)
A system for the automated detection of a seat occupancy state of a seat arrangement having at least one seat, wherein the system has a data processing device which is configured to carry out the method as claimed in claim 11 to detect the seat occupancy state (Claim 14)
A computer program or computer program product, comprising instructions which, when executed on a data processing device of a system, cause the system to carry out the method as claimed in claim 11 (Claim 15)
A vehicle, having: a seating arrangement having at least one seat; a radar sensor for radar scanning at least sections of the seating arrangement; and a system as claimed in claim 14 for the automated detection of a seat occupancy state of the seating arrangement as a function of a radar scan of at least sections of the seating arrangement carried out by the radar sensor (Claim 16).
Step 2A Prong 2: the additional elements, individually and in combination, fail to integrate the abstract idea into a practical application. Additional elements g and h merely apply the abstract idea to one or more generic computing components (see MPEP 2106.05(f)). Additional elements b, d, and f are merely necessary data input, and therefore insignificant extra pre-solution activity (see MPEP 2106.05(g)). Additional element e is merely data output, and therefore insignificant extra post-solution activity (see MPEP 2106.05(g)). Additional elements a, c, and i merely link the judicial exception to a particular technological environment (see MPEP 2106.05(h)).
Step 2B: the additional elements, individually and in combination, fail to amount to significantly more than the judicial exception because the Office takes Official Notice that they are well-understood, routine, and conventional activity previously known to the industry, specified at a high level of generality (see MPEP 2106.05(d)), or else they are insignificant pre-solution activity in the form of mere data gathering (additional elements b, d, and f) or insignificant post-solution activity in the form of mere data outputting (additional element e) (see numerous court decisions pertaining to observations, evaluations, judgements, and opinions, such as the findings from Electric Power Group where it was found that collecting information, analyzing it, and outputting certain results of the collection and analysis was not significantly more than the judicial exception – see MPEP 2106.05(d)(II)).
The Examiner recommends amending to include a discrete control step as a practical application to overcome the abstract idea rejections under 35 U.S.C 101 (As one possible example, Applicant could claim changing an airbag from activated to deactivated in response to the results).
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1, 3, 7-12, and 14-16 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by IEEE article “AI-Powered In-Vehicle Passenger Monitoring Using Low-Cost mm-Wave Radar” (Abedi et al., hereinafter, Abedi).
Abedi is Non-Patent Literature Document citation number 1 on the Information Disclosure Statement dated 6 June 2025 and already in the file wrapper as Non Patent Literature dated 6 June 2025 (15 pages).
Regarding Claim 1, Abedi discloses A method for producing an evaluation model for automated detection of a seat occupancy state of a seat arrangement having at least one seat (see at least “III. EXPERIMENTAL RESULTS” from page 19003-19011), wherein the method comprises:
providing parameters which are assigned to at least one of a plurality of specified possible seat occupancy states of the seat arrangement (see at least TABLE 3);
providing hyperparameters for the evaluation model, wherein the hyperparameters are configured to be adjusted on the basis of a metric (see at least TABLE 5, TABLE 6, and “D. MACHINE LEARNING ALGORITHMS” on page 19009: a range of values is specified for hyper-parameters, hyper-parameters are tuned / optimized);
determining a detection accuracy, wherein the detection accuracy indicates a discrepancy between the seat occupancy state assigned to the parameters and an evaluation result supplied by the evaluation model with the provided hyperparameters (see at least FIGURE 14);
providing a metric and evaluating the metric, wherein the metric takes into account a difference between the detection accuracy and a specified target value in order to output a value for a determined detection accuracy, wherein the target value indicates a detection accuracy at which the metric reaches an optimum; adjusting the hyperparameters, wherein the metric is optimized by means of an optimization method for this purpose (see at least TABLE 5, TABLE 6, and “D. MACHINE LEARNING ALGORITHMS” on page 19009: hyper-parameters are tuned / optimized); and
producing the evaluation model with the adjusted hyperparameters for training with training data for a method for the automated detection of a seat occupancy state of a seat arrangement having at least one seat (see at least “1) MULTICLASS CLASSIFICIATION” on page 19009 and “2) BINARY CLASSIFICATION (OVA)” on page 19009).
Regarding Claim 3, Abedi discloses the limitations of Claim 1. Furthermore, Abedi discloses comprising providing a set of parameters which are assigned to a plurality of seat occupancy states (see at least FIGURE 9), and the detection accuracy is in each case determined for combinations of parameters from the set of parameters (see at least FIGURE 13), wherein the metric is defined as an average which is formed for the set of parameters and the respective detection accuracy (“2) BINARY CLASSIFICATION (OVA)” on page 19009).
Regarding Claim 7, Abedi discloses the limitations of Claim 1. Furthermore, Abedi discloses wherein the seat arrangement has a plurality of seats, wherein the seat occupancy state is an individual or cumulative seat occupancy state of the seats (see at least “A. Our Equipment” on page 19003-19004 and “D. MACHINE LEARNING ALGORITHMS” on page 19006: radar installed in a Toyota Sienna with seven seats, monitors second and third row, determines occupancy for five seats).
Regarding Claim 8, Abedi discloses the limitations of Claim 1. Furthermore, Abedi further discloses wherein the seat occupancy state comprises at least one type of seat occupancy for at least one seat of the seat arrangement (see at least “D. MACHINE LEARNING ALGORITHMS” on page 19006: occupancy states for different seats determined, one type of seat occupancy is that the seat is occupied).
Regarding Claim 9, Abedi discloses the limitations of Claim 1. Furthermore, Abedi further discloses wherein the parameters further comprise a state of the vehicle (“D. MACHINE LEARNING ALGORITHMS” on page 19006: at least which seats are occupied in the vehicle is a state of the vehicle).
Regarding Claim 10, Abedi discloses the limitations of Claim 1. Furthermore, Abedi further discloses A method for training an evaluation model produced in accordance with the method as claimed in claim 1 for the automated detection of a seat occupancy state of a seat arrangement having at least one seat (see at least “III. EXPERIMENTAL RESULTS” from page 19003-19011), wherein the method comprises:
capturing measured data which represents an associated radar point cloud, wherein the radar point cloud is or has been obtained on the basis of radar scanning of at least sections of a spatial region encompassing the seat arrangement, and is assigned to one of a plurality of predefined possible seat occupancy states of the seat arrangement (see at least FIGURE 9 and FIGURE 10); and
producing training data from the measured data, wherein the training data is made available to the evaluation model as input data in order to obtain as its output an evaluation result which is assigned to the seat occupancy state of the seat arrangement (see at least Table 3: 32 situations of training data recorded).
Regarding Claim 11, Abedi discloses the limitations of Claim 10. Furthermore, Abedi further discloses A method for the automated detection of a seat occupancy state of a seating arrangement having at least one seat (see at least page 19002: machine learning algorithms to count passengers and identify occupied seats), wherein the method comprises:
capturing measurement data which represents an associated radar point cloud, wherein each radar point cloud is or has been obtained on the basis of radar scanning of at least sections of a spatial region encompassing the seat arrangement (see at least FIGURE 8);
determining a seat occupancy state of the seat arrangement by using an evaluation model which has been trained in accordance with the method as claimed in claim 10 and, depending on the radar point cloud, supplies one or more predefined possible seat occupancy states of the seat arrangement as an evaluation result (see at least “A. Our Equipment” on pages 19003-19004 and FIGURE 9: radar installed in a Toyota Sienna with seven seats, monitors second and third row, machine learning algorithm determines occupancy for seats #3, #4, #5, #6, and #7); and
issuing information defined as a function of the evaluation result (see at least “I. INTRODUCTION” on page 18998 and “IV. CONCLUSION” on page 19011: classification information issued).
Regarding Claim 12, Abedi discloses the limitations of Claim 11. Furthermore, Abedi further discloses wherein the issuing of the information comprises activating a signal source as a function of the information in order to cause the signal source to output a defined signal as a function of the activation, wherein the signal source is controlled in accordance with the information in such a way that it outputs a signal if the information results from an evaluation result according to which at least one seat of the seat arrangement is occupied and/or there is a selected predetermined seat occupancy state (see at least “I. INTRODUCTION” on page 18998 and “IV. CONCLUSION” on page 19011: classification information issued).
Regarding Claim 14, Abedi discloses the limitations of Claim 11. Furthermore, Abedi further discloses A system for the automated detection of a seat occupancy state of a seat arrangement having at least one seat, wherein the system has a data processing device which is configured to carry out the method as claimed in claim 11 to detect the seat occupancy state (see at least “A. Our Equipment” on page 19003-19004: computer system).
Regarding Claim 15, Abedi discloses the limitations of Claim 11. Furthermore, Abedi further discloses A computer program or computer program product, comprising instructions which, when executed on a data processing device of a system, cause the system to carry out the method as claimed in claim 11 (see at least “A. Our Equipment” on page 19003-19004 and “D. MACHINE LEARNING ALGORITHMS” on page 19006: machine learning algorithm on computer system).
Regarding Claim 16, Abedi discloses the limitations of Claim 14. Furthermore, Abedi further discloses A vehicle, having: a seating arrangement having at least one seat; a radar sensor for radar scanning at least sections of the seating arrangement; and a system as claimed in claim 14 for the automated detection of a seat occupancy state of the seating arrangement as a function of a radar scan of at least sections of the seating arrangement carried out by the radar sensor (see at least “A. Our Equipment” on page 19003-19004 and FIGURE 8: radar installed in a Toyota Sienna with seven seats, monitors second and third row).
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 2 and 4 are rejected under 35 U.S.C. 103 as being unpatentable over Abedi in view of U.S. Pub. No. 2021/0397859 (Arora et al., hereinafter, Arora).
Regarding Claim 2, Abedi discloses the limitations of Claim 1. Abedi does not explicitly disclose wherein the target value is smaller than a maximum detection accuracy, so that the value of the metric is a maximum for the target value.
Arora, in the same field of occupant detection, and therefore analogous art, teaches generalization techniques (see at least [0140]: “Generalization techniques can help reduce overfitting of machine-learned model 600 to the training data. Example generalization techniques include dropout techniques; weight decay techniques; batch normalization; early stopping; subset selection; stepwise selection; etc.”).
Therefore, wherein the target value is smaller than a maximum detection accuracy, so that the value of the metric is a maximum for the target value would have been obvious, before the effective filing date of the invention, with a reasonable expectation of success, to one having ordinary skill in the art, to avoid overfitting the data.
Regarding Claim 4, Abedi discloses the limitations of Claim 1. Abedi does not explicitly disclose wherein the metric includes the natural exponential function, which comprises a magnitude of a difference between the detection accuracy and the target value as an argument.
Arora, in the same field of occupant detection, and therefore analogous art, teaches a softmax function (see at least [0071] and [0074]), therefore, one of ordinary skill in the art would understand to use a natural exponential function and any use of a natural exponential function is a mathematical design choice, so wherein the metric includes the natural exponential function, which comprises a magnitude of a difference between the detection accuracy and the target value as an argument would have been obvious, before the effective filing date of the invention, with a reasonable expectation of success, to one having ordinary skill in the art. Applicant provides no unexpected benefit from the function.
Claim 5 is rejected under 35 U.S.C. 103 as being unpatentable over Abedi in view of U.S. Pub. No. 2022/0097636 (Beach et al., hereinafter, Beach).
Regarding Claim 5, Abedi discloses the limitations of Claim 1. Abedi does not explicitly wherein the optimization method is a Bayesian optimization.
Beach, in the same field of occupant detection in vehicles, and therefore analogous art, teaches wherein the optimization method is a Bayesian optimization (see at least [0029]: machine learning to determine if a child is in a vehicle may use Bayesian techniques).
It would have been obvious, before the effective filing date of the invention, with a reasonable expectation of success, to one having ordinary skill in the art, to substitute Bayesian techniques into Abedi because Bayesian techniques can be used to obtain predictable results of machine learning with the motivation of using a known method / comparing known methods.
Claim 6 is rejected under 35 U.S.C. 103 as being unpatentable over Abedi in view of IEEE Article “Detection and Localization of People Inside Vehicle Using Impulse Radio Ultra-Wideband Radar Sensor” (Lim et al., hereinafter, Lim).
Regarding Claim 6, Abedi discloses the limitations of Claim 1. Abedi does not explicitly wherein the optimization method is carried out with a specified number of iterations. Lim, in the same field of detection of occupants using radar, and therefore analogous art teaches wherein the optimization method is carried out with a specified number of iterations (see at least Fig. 10: optimization of number of classifiers with a fixed number of iterations).
It would have been obvious, before the effective filing date of the invention, with a reasonable expectation of success, to one having ordinary skill in the art, to substitute a limit on the number of iterations of Lim into Abedi with the motivation of limiting the amount of processing used.
Claim 13 is rejected under 35 U.S.C. 103 as being unpatentable over Abedi in view of U.S. Pub No. 2023/0168364 (Podkamien et al., hereinafter, Podkamien).
Regarding Claim 13, Abedi discloses the limitations of Claim 12. Furthermore, Abedi suggests seatbelt signaling based on the results (see at least “I. INTRODUCTION” on page 18998) but does not explicitly disclose further comprising:
detecting a seatbelt fastening state of at least one seat in the seating arrangement or receiving seatbelt information characteristic of this seatbelt fastening state;
wherein the signal source is activated as a function of the seatbelt information and the information from the evaluation result, in such a way that it outputs a seatbelt fastening warning signal if, according to the information, at least one seat of the seating arrangement is occupied and/or a selected specified seat occupancy state is present and seatbelt information indicates that the associated seatbelt is not fastened.
Podkamien, in the same field of occupant detection, and therefore analogous art, teaches further comprising: detecting a seatbelt fastening state of at least one seat in the seating arrangement or receiving seatbelt information characteristic of this seatbelt fastening state; wherein the signal source is activated as a function of the seatbelt information and the information from the evaluation result, in such a way that it outputs a seatbelt fastening warning signal if, according to the information, at least one seat of the seating arrangement is occupied and/or a selected specified seat occupancy state is present and seatbelt information indicates that the associated seatbelt is not fastened (see at least [0113]: “The processing unit 312 of the system 300, whether integrated with the radar transceiver array 310 or in data communication therewith, and possibly part of the onboard computer of the vehicle may interact with an onboard output device 318 such as a warning light or an audible signal such as an alarm beep or a verbal message. For example, noting that the passenger is not strapped in with the safety belt, or that a passenger in the front seat 324 is too small to be safely strapped in.”).
It would have been obvious, before the effective filing date of the invention, with a reasonable expectation of success, to one having ordinary skill in the art to provide a seatbelt warning with the motivation of reminder occupants of a vehicle to be safer (see at least Podkamien [0113]).
Additional Relevant Art
The prior art made of record and not relied upon is considered pertinent to applicant’s disclosure and may be found on the accompanying PTO-892 Notice of References Cited:
U.S. Pub No. 2021/0052176 which teaches “detecting occupants in a vehicle, and classifying those occupants according to the geometry of the vehicle, where sets of complex values associated with voxels in a predetermined region of the vehicle are converted into 3D complex images, where clusters of voxels in the 3D complex images are analyzed to determine presence of occupants, and where positions of seats may determine which seats in the vehicle are occupied”.
U.S. Pub No. 2023/0219578 which teaches “a computer having a processor and a memory, the memory storing instructions executable by the processor to access sensor data from one or more imaging radar sensors of a vehicle, identify, based on the sensor data, a radar point cloud corresponding to an occupant of the vehicle, analyze the radar point cloud to determine an occupant classification of the occupant, determine an operating parameter for a feature of the vehicle based on the occupant classification, and implement the operating parameter for the feature”.
Conclusion
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/JASON HOLLOWAY/Primary Examiner, Art Unit 3658
/A.R.M./Examiner, Art Unit 3658
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