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 communication filed 10/23/2025.
Response to Arguments
Applicant's arguments filed 10/23/2025 have been fully considered but they are not persuasive.
With regard to the arguments on pages 14-19 directed towards the previous 112 rejections,
As to Claims 1-20,
Applicant initially points to paragraphs 129 and 130 for support, but the Examiner respectfully disagrees that these paragraphs reasonably provide support for classifying as claimed. In these and the related paragraphs, applicant does provide a formula, but this formula is a proximity formula that merely allows for the detection of the relative position of an object. Applicant mentions that colors represent different materials, but nothing in these or the related paragraphs reasonably explain the manner in which applicant is actually classifying any of the materials. That is, nothing in these paragraphs or any related paragraphs reasonably explain the manner in which applicant identifies one type of material over another. The only disclosure that reasonably pertains to any form of classification is the broad discussion stating that the classification includes evaluating a probability of the object properties, but the disclosure does not reasonably explain the manner in which applicant performs this evaluation or provide any reasonable explanation as to the manner in which any probability is implemented.
Applicant argues that a skilled person can decide what level of generality is desired to classify the material and that materials are not perfect and there will be tolerances and variations to accommodate that is also within the skill of one of ordinary skill in the art. The Examiner respectfully notes that applicant is arguing what applicant believes a person of ordinary skill in the art would know but without any necessary evidence (see MPEP 2145(I)). That stated, whether a person of ordinary skill in the art would decide the manner in which they desired to implement the device, such a person would nevertheless fail to recognize the manner in which applicant is implementing the claim feature in order to establish possession.
In order to establish possession, applicant must provide a sufficient explanation such that a person of ordinary skill in the art would reasonably recognize what applicant is doing. The test here is not whether such a person could figure out a way to implement the claim, but rather, whether they would recognize applicant’s way of implementing the claim. In the instant case, such a person would not reasonably recognize the manner in which applicant evaluated a probability, including what probabilities would and would not be sufficient to reasonably identify any particular material, so as to reasonably demonstrate possession of the claim features.
Applicant argues the machine learning features in the disclosure arguing that various machine learning algorithms exist which can be trained on reference states to be able to provide classification. Here, the Examiner respectfully notes that applicant is arguing what applicant believes a person of ordinary skill in the art would know but without evidence (see MPEP 2145(I)). That stated, applicant has furthermore, respectfully, not identified any machine learning algorithm that was well-known to be used for the claimed classification. In order to rely upon that which is well-known in the art, applicant must still reasonably apprise a person of ordinary skill in the art of the manner in which applicant is implementing a claim feature. As has been held, “What is conventional or well known to one of ordinary skill in the art need not be disclosed in detail. See Hybritech Inc. v. Monoclonal Antibodies, Inc., 802 F.2d at 1384, 231 USPQ at 94” (MPEP 2163). As such, when relying upon that which is well-known in the art, applicant is not required to provide a detailed explanation, but applicant must still reasonably demonstrate what applicant is doing with a sufficient level of explanation. For example, if a machine learning algorithm existed that was well-known to be used for classification purposes in the related art, then applicant need only reference such an algorithm. Doing so would reasonably demonstrate what applicant is doing (written description), with no further detail necessary because a person of ordinary skill in the art would reasonably understand how to implement this well-known algorithm. However, when the original disclosure does not provide any algorithm or provide a sufficient level of guidance or explanation demonstrating what applicant is doing, the disclosure lacks proper written description.
In the instant case, applicant’s mere reference to machine learning does not reasonably demonstrate proper written description as machine learning is a broad concept that does not reasonably identify any particular well-known manner of implementing the classifying. The Examiner acknowledges that the disclosure mentions that an artificial network can be constructed from various layers, but such an explanation does not reasonably demonstrate possession because an artificial network, even formed from layers, is a broad concept without reasonably level of specificity to demonstrate what this network is or the manner in which applicant is implementing it. The test here is not whether a person of ordinary skill in the art can figure out how to implement such a network. Instead, the test for written description is whether such a person would recognize the network that applicant is using. However, by not providing any reasonable level of detail of the network, the original disclosure does not reasonably provide proper written description.
From the vantage point of enablement, the Examiner respectfully notes that applicant does not identify any complete example of how applicant classifies. Applicant does not provide any complete example of any artificial network, model, or identify any specific formula, model, or other mechanism that was well-known to be used to classify in the claimed manner such that a person of ordinary skill would recognize how to implement the claim features. Artificial models, machine learning, or other related concepts are just that, concepts or models, and identifying these concepts does not reasonably also identify any particular manner that was well-known to be used for the claimed purpose. For example, merely that an equation can be used to determine a value, but where no reasonable explanation was provided about that equation, would not reasonably demonstrate either enablement or written description. This is because if the original disclosure was completely silent or partially silent at that equation, no complete example would be present, the equation would not reasonably have been established to be well-known for the intended purpose, and a person of ordinary skill in the art would therefore have to figure out all of part of the equation in order to figure out how to implement the claim. Such a burden would reasonably be undue.
As to Claim 20,
Applicant disagrees with the Examiner’s previous explanation regarding the conveyor, but the Examiner respectfully disagrees with applicant. The interpretation provided by the Examiner is based in the disclosure, where in paragraph [0142], applicant expressly states that the conveyor is a conveyer belt. While applicant may intend the conveyor to be some other aspect of a larger conveying apparatus, by expressly providing the conveyor belt as the conveyor, then any sections claims of the conveyor would be sections of the belt itself. The interpretation provided by the Examiner is therefore consistent with the disclosure. Furthermore, no explanation of any kind pertaining to the conveyor is provided, including what is meant or the manner in which any direction change is implemented as claimed.
Applicant argues that the direction change is a structural feature, but applicant, respectfully, does not reasonably explain such a feature other than to argue that the change is between the first and second section. The term “change” is not reasonably a noun, and is not reasonably identifying a particular object. While applicant makes such an argument, applicant does not present any evidence to demonstrate that a “change” would reasonably be recognized in the art as a structural object. Indeed, no reasonable definition of the term “change” as a noun would reasonably be a physical object or portion of a physical object as applicant is arguing. Instead, such a term is well-known to mean, amongst other definitions, “ The act, process, or result of altering or modifying” per https://www.ahdictionary.com/word/search.html?q=change, but where such a definition would not be reasonable. While a direction change may describe what happens to a physical object, in that it changes direction, a directional change itself does not reasonably identify any particular object.
Furthermore, the Examiner respectfully notes that this feature was also rejected because no disclosure of the claimed conveyor is reasonably provided, and applicant does not reasonably demonstrate proper written description for such a feature. Merely stating that a conveyor is present does not reasonably disclose the manner in which it is implemented.
As to the enablement rejections,
Applicant argues that paragraphs [0034] and [0035] demonstrate enablement, but the Examiner respectfully disagrees. First, the cited paragraphs provide no explanation as to how applicant is classifying. The inverse dependence features is only disclosed with regard to how the reference standard is obtained. That stated, this disclosure does not reasonably disclose how the reference standard is obtained. What paragraph [0035] is stating that is that applicant calculates a standard by relying upon a inverse dependence. This paragraph does state that the dependence is “on the square of the amplitude difference,” but this does not reasonably mean that that standard itself is calculated as the square the amplitude difference. Instead, it is stating that it uses or is based upon such a difference, but where the actual calculation is, respectfully, not reasonably disclosed. Furthermore, even to the extent that the standard was calculated from the above amplitude difference, the original disclosure does not reasonably state what this amplitude difference or from where it is obtained.
Applicant argue that a complete mathematical formula is provided, but the Examiner respectfully disagrees. This formula is expressly disclosed to be a proximity formula that identifies a distance (d) to an object. Such a formula is not disclosed to be a classification formula, so as to allow the classification of a material, nor is there any reasonable explanation as to how this formula can be used for such a purpose. Applicant argues that working examples are provided, but the Examiner respectfully disagrees. What these figures show are plots of materials at different points in a plane. Whether these points and plots were or were not obtained using the argued equation, what is not reasonably disclosed is how applicant 1) obtains these plots, and to the point that it is based upon the equation, 2) what applicant does with any of this data to then be able to identify or classify any particular material. Applicant provides no explanation as to any thresholds of probability, addressing materials that may generate similar values in permeability, or any other reasonable explanation to demonstrate how applicant classifies using any of the obtained data.
The Office Action is not improperly demanding disclosure of specific values and ranges. What the Office Action is noting is that applicant does not reasonably explain what applicant does to classify materials using any obtained data. When applicant is not relying upon a well-known manner implementing a claim feature, and there is no evidence of such a feature, then applicant must reasonably explain what applicant is doing in order to demonstrate enablement. After obtaining data, stating that a probability can be used to identify material, without any further guidance, is not reasonably demonstrating enablement. This is because applicant is not providing any reasonable guidance as to how such a feature should be implemented, and is therefore leaving it up to a person of ordinary skill in the art to figure out what types of probability equations could be used, what values would and would not be acceptable for any particular material, and every other aspect of the process.
As to the arguments directed towards machine learning, the Examiner respectfully disagrees and directs applicant to the above response pertaining to machine learning. Merely nothing that a machine learning algorithm can be used, including layers, does not reasonable establish that algorithm. When such a feature is not well-known in the art, applicant must reasonably demonstrate how applicant is implementing the algorithm. Here, there is no discussion as to how the layers are implemented, how any weighting factors are established, what nodes and many nodes are implemented, or any other reasonable amount of detail for the model. While applicant is permitted to rely upon that which is well-known in the art, such a feature must be well-known in the art. Here, applicant has not reasonably established that the type of model applicant intends to rely upon to classify was well-known, and thus further detail is required. Without such detail, a person of ordinary skill in the art would have to independently figure out how the implement the model and the claimed classifying as part of the model, creating an undue burden.
As such, the Examiner respectfully disagrees.
With regard to the arguments directed towards the prior art Chalupczak et al. (Chalupczak) (GB 2575695 A) on pages 20-23,
Applicant disagrees with the Examiner’s interpretation of a conveyor, but the Examiner respectfully notes that applicant does not explain why the interpretation made by the Examiner is unreasonable. A conveyor is a device that moves an object from one location to another. Indeed, the definition of a conveyor is “One that conveys, especially a mechanical apparatus that transports materials, packages, or items being assembled from one place to another” per https://www.ahdictionary.com/word/search.html?q=conveyor. Such a definition is consistent with applicant’s conveyor. While this definition is broadest, it is reasonable. As such, any device, including a stage, that “conveys” an object from one locator to another is reasonably a conveyor. Furthermore, the Examiner has explained that the stage is can move in two dimensions and thus can change the direction of the moving sample, and the sections are the part of the overall system that the stage moves the sample from and two, such as the initial location of the system where the sample is placed onto the stage, to the location of the system where the sample is removed, can be the claimed sections. Lastly, the phrase “directional change” is not clear as explained above, but the Examiner respectfully notes that the stage is a two axis stage, and thus must have a “directional change” as it will change direction while conveying. While applicant may desire the use of the term “bend” as opposed to directional change, such a feature is not claimed. A conveyor can comprise a direction change when it includes moving in more than one direction. A directional change is not reasonably limited to or even reasonably defined to be a physical object. Applicant does not clearly redefine the term “change” to accommodate such a definition, and merely providing an example of what applicant desires a change to be does not reasonably redefine the term.
Applicant then argues that the requirement that the measurement location be located at the direction change of the conveyor is not met, but the Examiner respectfully disagrees and notes that no such feature is required in the claim. Claim 20 does state the magnetic field source “for providing” the primary magnetic field at the measurement location located at the direction change, but such a feature is only claiming an intended use of the primary magnetic field. Such a feature is not a positive recitation of what follows the term “for providing” and instead is claiming where the magnetic field source must be usable. As such, what is required in this claim feature is that the primary magnetic field must be able to be positioned at a measurement location at a directional change, but it no actual field must be located at this location and no location must actually be at a directional change.
Additionally, the phrase “the measurement location located at the directional change” is indefinite and unclear. No such location was previously recited, as the previous measurement location was not previously claimed to be at the directional change. It is therefore unclear what measurement location this phrase is referencing.
The prior art reasonably discloses the claim feature, and the Examiner therefore respectfully disagrees.
As to Claims 1, 2, and 5-19,
Applicant argues that Chalupczak does not disclose rotating the bias field discloses measuring for a plurality of angles of the first direction. The Examiner respectfully disagrees.
While the Examiner acknowledges that it is the bias field that rotates, it is the measurements of the secondary field relative to the first direction and measurement direction that this claim feature is directed towards. The first direction cannot change, otherwise it would no longer be the first direction. As such, this phrase must reasonably be referring to how measurements are made where something about these measurements have more than one angle relative to the first direction. Any magnetic field detected at the sensor will be due to the combination of the primary and bias magnetic fields. Meaning, because the bias magnetic field has changed direction, the measured magnetic field at the sensor must also have changed direction, and that direction will be different relative to the first direction. If nothing changed, then the sensor would not detect a difference when the bias field changes direction. As a parallel, if the sensor were rotated or otherwise oriented in more then one way relative to the primary field while making measurements, then such a disclosure would also teach measuring the secondary magnetic field for a plurality of angles with respect to the measurement location. To the extent that applicant desires the first direction itself to have plural angles, the Examiner respectfully notes that such a feature is not claimed.
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 1-15 and 18-20 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.
MPEP 2163.03(V) “While there is a presumption that an adequate written description of the claimed invention is present in the specification as filed. In re Wertheim, 541 F.2d 257, 262, 191 USPQ 90, 96 (CCPA 1976), a question as to whether a specification provides an adequate written description may arise in the context of an original claim. An original claim may lack written description support when (1) the claim defines the invention in functional language specifying a desired result but the disclosure fails to sufficiently identify how the function is performed or the result is achieved or (2) a broad genus claim is presented but the disclosure only describes a narrow species with no evidence that the genus is contemplated. See Ariad Pharms., Inc. v. Eli Lilly & Co., 598 F.3d 1336, 1349-50 (Fed. Cir. 2010) (en banc). The written description requirement is not necessarily met when the claim language appears in ipsis verbis in the specification. "Even if a claim is supported by the specification, the language of the specification, to the extent possible, must describe the claimed invention so that one skilled in the art can recognize what is claimed. The appearance of mere indistinct words in a specification or a claim, even an original claim, does not necessarily satisfy that requirement."Enzo Biochem, Inc. v. Gen-Probe, Inc., 323 F.3d 956, 968, 63 USPQ2d 1609, 1616 (Fed. Cir. 2002)” (emphasis added).
“The question is not whether a claimed invention is an obvious variant of that which is disclosed in the specification. Rather, a prior application itself must describe an invention, and do so in sufficient detail that one skilled in the art can clearly conclude that the inventor invented the claimed invention as of the filing date sought … One shows that one is "in possession" of the invention by describing the invention, with all its claimed limitations, not that which makes it obvious. Id. ("[T]he applicant must also convey to those skilled in the art that, as of the filing date sought, he or she was in possession of the invention. The invention is, for purposes of the 'written description' inquiry, whatever is now claimed.") (emphasis in original). One does that by such descriptive means as words, structures, figures, diagrams, formulas, etc., that fully set forth the claimed invention” (Lockwood v. Am. Airlines, Inc., 107 F.3d 1565, 1572 (Fed. Cir. 1997)).
As to Claim 1,
The phrase “classifying the orthogonal component of the secondary magnetic field with reference to a material or material combination type and thereby identifying said type of material or material combination in the measurement location” on lines 8-11 lacks proper written description.
At issue here is that applicant does not reasonably explain the manner in which applicant classifies or otherwise identifies any particular material, type of material, or material combination as claimed. The Examiner acknowledges that applicant, as part of the classification process, does disclose the use of a formula as seen in paragraph [0130], and that this formula essentially represents a difference between an amplitude of a pixel of the test object itself and an amplitude of a pixel of a reference object. As such, applicant with this formula is generating different values depending on how closely the test object matches any particular reference. However, applicant does not disclose what applicant then does after this to reasonably classify or otherwise identify the particular material, type of material, or material composition. For example, applicant does not reasonably explain what values from this equation would and would not be considered or identified as any particular material. Applicant does disclose the use of similarity to a reference standard to classify a material (see paragraph [0126] for example), but applicant does not reasonably disclose what this similarity is such as how similar a test objected must be in value to be classified as any particular material or composition. Applicant does mention the use of machine learning and deep learning in paragraphs [0135] and [0136], but these paragraphs do not reasonably define the similarity, such as how similar an object must be to be considered any particular material or composition, and applicant does not reasonably provide a complete disclosure of the algorithm, model, or neural network used to overcome this issue. Note that the test for proper written description is not whether a person of ordinary skill in the art can figure out some way to implement the claim features. Instead, the test for proper written description is whether a person of ordinary skill in the art would reasonably recognize the specific manner that applicant is implementing the claim feature. While applicant may rely upon that which is well known in the art, to rely upon that, applicant must first demonstrate at least one example of a well-known device (or model) that was well-known for the intended purpose. Meaning, merely identifying that a neural network or machine learning is used is not reasonably specific, as these models are high level descriptions, and applicant has not identified any specific model that was well-known to be used for this purpose. For example, if applicant were minimizing a difference between a measured and model value to identify the object generating the measured value, and applicant merely disclosed that a least squares approach were utilized, then such a disclosure alone would reasonably establish proper written description because least squares is a specific and well-known modeling technique to minimize a difference. However, least squares is an example of machine learning, and merely mentioning machine learning instead of least squares would not reasonably establish proper written description as the phrase “machine learning” is broad and does not reasonably identify a specific well-known technique for the intended purpose. As such, the above phrase lacks proper written description.
As to Claim 13,
The phrase “classifying the orthogonal component of the secondary magnetic field includes comparing the orthogonal component of the secondary magnetic field to an at least one reference standard and classifying based on a similarity to said at least one reference standard” on lines 1-4 lacks proper written description.
Similar to that already explained in the above rejection of Claim 1, which is incorporated herein, applicant does not reasonably explain the manner in which applicant performs the above classification, including basing the classification on a similarity. While applicant provides a formula to show how different values can be obtained depending upon the sample, applicant does not reasonably then explain what is done with this formula in the claimed manner to allow for the claimed classification. Applicant, for example, does not reasonably explain how similar the values obtained need to be to any particular material or composition thereof to thereby identify or classify that particular material. Meaning, applicant does not reasonably explain what values would indicate, for example, that steel has been detected and to thereby classify that material as steel. Applicant does not explain how similar any measured or determined values must be to make such a classification, or to classify any other particular material. As such, this phrase lacks proper written description.
As to Claim 15,
The phrase “classifying the orthogonal component of the secondary magnetic field is performed by a machine learning algorithm trained on a plurality of reference standards, wherein the plurality of reference standards is a plurality of reference images and/or reference image fragments” on lines 1-4 lacks proper written description. While applicant does provide some details of the machine learning / neural network, at no point does applicant provide a complete example of any machine learning algorithm, model, neural network, or deep learning algorithm. The original disclosure does not provide the claimed algorithm, and a person of ordinary skill in the art would not reasonably recognize the manner in which the above machine learning algorithm was implemented, including one based on one or more reference standards, to demonstrate possession of the claim feature. Note that the phrase “machine learning” is a broad concept that does not reasonably identify any particular algorithm or model, especially one that was both well-known in general and well-known to be usable for the claimed purpose. As such, this phrase lacks proper written description.
As to Claim 20,
The phrase “a conveyor comprising a first section, a second section and a directional change between the first section and the second section, the conveyor being configured to convey objects from the first section to the second section via the directional change; and the magnetic field source for providing the primary magnetic field primarily in the first direction into the measurement location located at the directional change of the conveyor” on lines 2-8 lacks proper written description. At issue here is that applicant does not provide any details of the conveyor or reasonably disclose the manner in which it or the directional change is implemented. This issue is raised because a conveyor belt, which is what the conveyor is in light of the disclosure, would not reasonably have two sections such that a sample could or would be moved from one section to another. This is because the sample is stationary with respect the belt as the sample would move with the belt. As such, the sample would also be on the same section at all times. Furthermore, no explanation is provided as to the manner in which any bend (directional change) would be able to cause an object or sample to be moved between the two sections as claimed. No other disclosure of the conveyor is provided, and thus the original disclosure does not reasonably provide proper written description for the above claim feature. For the purpose of compact prosecution, the Examiner is interpreting this phrase to mean that any conveying system that can move an object or sample between two positions reasonably discloses the claim feature.
As to Claims 2-15, 18, and 19,
These claims stand rejected for incorporating and reciting the above rejected subject matter of their respective parent claim(s) and therefore stand rejected for the same reasons.
Claims 1-15 and 18-20 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, because the specification does not reasonably provide enablement for 1) “classifying the orthogonal component of the secondary magnetic field with reference to a material or material combination type and thereby identifying said type of material or material combination in the measurement location” in the last paragraph of Claim 1; 2) “classifying the orthogonal component of the secondary magnetic field includes comparing the orthogonal component of the secondary magnetic field to an at least one reference standard and classifying based on a similarity to said at least one reference standard” on lines 1-4 of Claim 13, and 3) “classifying the orthogonal component of the secondary magnetic field is performed by a machine learning algorithm trained on a plurality of reference standards, wherein the plurality of reference standards is a plurality of reference images and/or reference image fragments” on lines 1-4 of Claim 15.
Specifically, the Examiner does not find any explanation as to how applicant is implementing the above classification or identification of a material or material combination as claimed, and applicant does not reasonably provide sufficient detail of how applicant is implementing the claimed machine learning algorithm. This is a scope of enablement rejection because the specification does not enable one of ordinary skill to use the invention commensurate with the scope of the claims without undue experimentation.
There are many factors to be considered when determining whether there is sufficient evidence to support a determination that a disclosure does not satisfy the enablement requirement and whether any necessary experimentation is “undue.” These factors include, but are not limited to:
(A) The breadth of the claims;
(B) The nature of the invention;
(C) The state of the prior art;
(D) The level of one of ordinary skill;
(E) The level of predictability or unpredictability in the art;
(F) The amount of direction or guidance presented by invent tor;
(G) The existence or absence of working examples; and
(H) The quantity of experimentation necessary.
See In re Wands, 8 USPQ2d 1400, 1404 (Fed. Cir. 1988);
MPEP §2164.01(a)
As to factor (A), the Examiner notes that the claims 1-20 are unbounded. Applicant has not provided any explanation as to how applicant is implementing the above classification after determining a value using the equations in paragraphs [0130]-[0132], as these equations alone do not reasonably define a similarity or other necessary range of values needed to be able to reasonably identify any particular material or material composition, and similarly, applicant does not reasonably provide any complete example of what the machine learning algorithm is or how applicant implements it based on one or more reference standards, and as such claims 1-20 would cover any and every way possible to accomplish the claimed features.
As to factor (G), the Examiner notes that applicant has not provided sufficient working examples via the specification commensurate with the scope of the claims. Applicant does not provide any example of how applicant classifies or otherwise identifies a material or material composition once a value from the formula in paragraph [0130] is implemented. The specification is silent as to how applicant identifies any particular material or material composition from this formula or using any other means. Applicant explains in paragraph [0132] that the smaller the integrated amplitude difference is, the higher its inverted value and the closer its normalizes value approaches 1. However, what applicant does not reasonably explain is when the value does not perfectly equal 1, how applicant uses this formula or any other feature of the disclosure to reasonably classify and identify any material or material composition. As best understood, applicant is using a range of undisclosed values to make such a classification, because applicant discloses that the classification is based upon a similarity. As such, the closer the value is to 1, the more likely it is any particular material or composition. However, without any range of values for what constitutes the similarity, a person of ordinary skill in the art would not reasonably know how applicant is making such a determination. Therefore, the specification fails to disclose any suitable and sufficient working examples to perform the above claimed classification, identification, and where such is based upon a similarity. Similarly, applicant does not reasonably provide the claimed machine learning algorithm or it is based on one or more reference standards as claimed. While applicant provides some neural network details, applicant does not reasonably provide a complete neural network or other machine learning algorithm nor identify any well-known algorithm that can be used for the claimed purpose. Therefore, the specification fails to disclose any suitable and sufficient working examples to implement the above machine learning algorithm.
As to factor (H), the Examiner notes that the quantity of experimentation need is high. Applicant provides no examples or explanation as to how applicant is implementing the claimed classification, identification, and where such is based upon a similarity. Applicant further does not provide any details of this similarity, or a complete example of the classification as claimed. Thus, one having ordinary skill in the art would have to independently the types or ranges of values that would reasonably indicate any particular material, and do this for the full range of materials or compositions that are desired to be detected. Such a person would independently, through trial and error, have to and independently what values would and would not reasonably indicate any particular material or material composition, including the development of the software needed to perform or accomplish the claimed functions. Similarly, applicant does not provide a complete example or explanation as to how applicant is implementing the claimed machine learning language using the reference standards Applicant further does not provide the actual algorithm, model, or identify one that was well-known for the claimed purpose. Thus, one having ordinary skill in the art would have to independently what types of machine learning algorithms could be used for the claimed purpose, including ones based on one or more reference stands, and do this for the full range of materials or compositions that are desired to be detected. Such a person would independently, through trial and error, have to and independently algorithms would and would not reasonably indicate any particular material or material composition, including the development of the software needed to perform or accomplish the claimed functions.
A person of ordinary skill in the art would have to independently figure out how to implement the above claim features.
In view of the forgoing, the Examiner finds that the unbounded modes of operation are directed to an invention for which no working examples have been provided commensurate with the scope of the claims. Based on the Wands factors (A), (G), and (H), the Examiner concludes that applicant's specification does not enable those skilled in the art to make and use the full scope of the claimed invention without undue experimentation. The Examiner notes that the claimed features noted above encompass any and all structures and/or acts for achieving their results and operation, including those which were not what the applicant had invented and those which could be invented in the future. As such, claims 1-20 are rejected under 35 U.S.C. §112(a) for lacking an enabling disclosure commensurate with the scope of the claims.
As to Claims 2-15, 18, and 19,
These claims stand rejected for incorporating the above rejected subject matter of their respective parent claims and therefore stand rejected for the same reasons.
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-15 and 18-20 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.
As to Claim 1,
The phrase “classifying the orthogonal component of the secondary magnetic field with reference to a material or material combination type and thereby identifying a type of material or material combination in the measurement location” on lines 8-11 is indefinite.
Similar to that already explained in the above rejection of Claim 1, which is incorporated herein, applicant does not reasonably explain the manner in which applicant performs the above classification, which in light of the disclosure, requires a classification based on a similarity of one determined value to a reference value. While applicant provides a formula to show how different values can be obtained depending upon the sample, applicant does not reasonably then explain what is done with this formula in the claimed manner to allow for the claimed classification. Applicant, for example, does not reasonably explain how similar the values obtained need to be to any particular material or composition thereof to thereby identify or classify that particular material. Meaning, applicant does not reasonably explain what values would indicate, for example, that steel has been detected and to thereby classify that material as steel. Applicant does not explain how similar any measured or determined values must be to make such a classification, or to classify any other particular material. As such, it is unclear what values or similarity is or is not sufficient to reasonably classify or otherwise identify any particular object as no such guidance is been provided, but where such guidance is reasonably required in order to perform the claim feature.
As to Claim 13,
The phrase “classifying the orthogonal component of the secondary magnetic field includes comparing the orthogonal component of the secondary magnetic field to an at least one reference standard and classifying based on a similarity to said at least one reference standard” on lines 1-4 is indefinite.
Similar to that already explained in the above rejection of Claim 1 in the 112(a) section, which is incorporated herein, applicant does not reasonably explain the manner in which applicant performs the above classification, including basing the classification on a similarity. While applicant provides a formula to show how different values can be obtained depending upon the sample, applicant does not reasonably then explain what is done with this formula in the claimed manner to allow for the claimed classification. Applicant, for example, does not reasonably explain how similar the values obtained need to be to any particular material or composition thereof to thereby identify or classify that particular material. Meaning, applicant does not reasonably explain what values would indicate, for example, that steel has been detected and to thereby classify that material as steel. Applicant does not explain how similar any measured or determined values must be to make such a classification, or to classify any other particular material. As such, it is unclear what values or similarity is or is not sufficient to reasonably classify or otherwise identify any particular object as no such guidance is been provided, but where such guidance is reasonably required in order to perform the claim feature.
As to Claim 18,
The phrase “a step of classifying the orthogonal component of the secondary magnetic field with reference to a material or material combination type and thereby identifying a type of material or material combination in the measurement location” on lines 4-7 is indefinite. Claim1 already recites classifying the orthogonal component on lines 8-11. As best understood, this Claim 1 recitation is referring to the same step of classifying as now distinctly recited in Claim 18. The difference and relationship between these two claim recitations are therefore unclear, as applicant is twice reciting the same feature that is not performed twice, thus rendering the claim indefinite.
As to Claim 20,
The phrase “a conveyor comprising a first section, a second section and a directional change between the first section and the second section, the conveyor being configured to convey objects from the first section to the second section via the directional change; and the magnetic field source for providing the primary magnetic field primarily in the first direction into the measurement location located at the directional change of the conveyor” on lines 2-8 is indefinite.
1) Applicant is claiming that the conveyor comprises “a directional change,” but a directional change is not reasonably a physical object. While applicant describes such a feature in the disclosure as a bend, such a use of this phrase is not consistent with the plain and ordinary meaning of the phrase. A directional change is reasonably a change in direction, and is not reasonably a physical object. Furthermore, while applicant provides an example of the directional change, it is unclear if such a direction change is limited to this bend, in that it is unclear what other features or movements would or would not reasonably constitute a directional change. For the purpose of compact prosecution, the Examiner is interpreting that any conveying object that can reasonably include conveying an object in more than one direction reasonably constitutes a directional change.
Where applicant acts as his or her own lexicographer to specifically define a term of a claim contrary to its ordinary meaning, the written description must clearly redefine the claim term and set forth the uncommon definition so as to put one reasonably skilled in the art on notice that the applicant intended to so redefine that claim term. Process Control Corp. v. HydReclaim Corp., 190 F.3d 1350, 1357, 52 USPQ2d 1029, 1033 (Fed. Cir. 1999). The term “directional change” in claim 20 is used by the claim to mean “a bend,” while the accepted meaning is “a change in direction.” The term is indefinite because the specification does not clearly redefine the term.
2) The phrase “the measurement location located at the directional change of the conveyor” in the above phrase is indefinite. No measurement location located at the direction change was previously recited. While a measurement location was previously claimed, it was not claimed to be at any specific location. As such, it is unclear what measurement location located at the directional change of the conveyor this phrase is referencing, and the relationship between this phrase and the previously recited measurement location are unclear.
As to Claims 2-15, 18, and 19,
These claims stand rejected for incorporating the above rejected subject matter of their respective parent claims and therefore stand rejected for the same reasons.
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.
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.
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.
Claims 1, 2, and 5-15, 18, 19, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Chalupczak et al. (Chalupczak) (GB 2575695 A) in view of Girrell et al. (Girrell) (US 2017/0307500).
As to Claims 1, 13, and 15,
Chalupczak discloses A method of identifying a type of material or material combination in a measurement location, comprising: providing a primary magnetic field primarily in a first direction into the measurement location (Figures 1(a),6 / note the first direction is from (14) towards the sample, and parallel to bias field 26), (Page 10, Line 9-14); measuring an orthogonal component of a secondary magnetic field, the orthogonal component of the secondary magnetic field being in a direction substantially orthogonal to the first direction (Page 16, Lines 16-23); and using the orthogonal component and measurements to detect the material conductivity and/or permeability of the material (Page 32, Lines 24-28), (Page 24, Lines 4-5 / note Figure 6 is the same as Figure 1(a) except for the differences discussed, and thus referencing common features from Figure 1(a) is reasonable), wherein measuring the orthogonal component of the secondary magnetic field includes measuring the orthogonal component of the secondary magnetic field for a plurality of angles of the first direction with respect to the measurement location (Page 6, Lines 7-12), (Figure 6 / note by rotating the bias field, the orthogonal component of the secondary field will be measured over a plurality of angles as claimed).
Chalupczak does not disclose using the material conductivity and/or permeability to identify the type of material or material combination, and thus does not disclose classifying the orthogonal component of the secondary magnetic field with reference to a material or material combination type and thereby identifying a type of material or material combination in the measurement location, classifying the orthogonal component of the secondary magnetic field includes comparing the orthogonal component of the secondary magnetic field to an at least one reference standard and classifying based on a similarity to said at least one reference standard, classifying the orthogonal component of the secondary magnetic field is performed by a machine learning algorithm trained on a plurality of reference standards, wherein the plurality of reference standards is a plurality of reference images and/or reference image fragments, a program configured to perform, when executed on a computing device as part of a method according to claim 1, a step of classifying the orthogonal component of the secondary magnetic field with reference to a material or material combination type and thereby identifying a type of material or material combination in the measurement location.
Girrell discloses using the material conductivity and/or permeability to identify the type of material or material combination, and classifying induced secondary magnetic field with reference to a material or material combination type and thereby identifying a type of material or material combination in the measurement location (Paragraphs [0022],[0039]-[0041]), classifying the orthogonal component of the secondary magnetic field includes comparing the orthogonal component of the secondary magnetic field to an at least one reference standard and classifying based on a similarity to said at least one reference standard (Paragraphs [0022],[0039]-[0041]), (Claim 15), classifying the orthogonal component of the secondary magnetic field is performed by a machine learning algorithm trained on a plurality of reference standards, wherein the plurality of reference standards is a plurality of reference images and/or reference image fragments (Paragraph [0026] / note that the learning system must have been trained on more than one reference because it can identify more than one material, and such trained data can reasonably be considered an image or image fragment as such an image or image fragment is merely stored data), and that the classification is performed using a computing device executing software (Paragraph [0026]).
It would have been obvious to a person of ordinary skill in the art before the effective filing date to modify Chalupczak to include using the material conductivity and/or permeability to identify the type of material or material combination, and thus disclose classifying the orthogonal component of the secondary magnetic field with reference to a material or material combination type and thereby identifying a type of material or material combination in the measurement location, classifying the orthogonal component of the secondary magnetic field includes comparing the orthogonal component of the secondary magnetic field to an at least one reference standard and classifying based on a similarity to said at least one reference standard, classifying the orthogonal component of the secondary magnetic field is performed by a machine learning algorithm trained on a plurality of reference standards, wherein the plurality of reference standards is a plurality of reference images and/or reference image fragments, a program configured to perform, when executed on a computing device as part of a method according to claim 1, a step of classifying the orthogonal component of the secondary magnetic field with reference to a material or material combination type and thereby identifying a type of material or material combination in the measurement location. given the above disclosure and teaching of Girrell in order to advantageously provide an accurate determination or identification of the particular type of material (Paragraph [0004]).
(Note: Girrell is reasonably classifying the secondary magnetic field with reference to a material type because it is using the secondary magnetic field to obtain permeability, which is then correlated to a reference to identify the particular material. Furthermore, because Chalupczak only uses the orthogonal component of the secondary magnetic field, in the combination, the prior art would reasonably disclose the above claim feature).
As to Claim 2,
Chalupczak discloses measuring the orthogonal component of the secondary magnetic field is performed with an anisotropic sensor (atomic magnetometer) configured to have lesser sensitivity to magnetic fields in the first direction (Figure 6), (Page 23, Lines 7-12).
As to Claim 6,
Chalupczak discloses measuring the orthogonal component of the secondary magnetic field includes performing a spatial scan of the measurement location (Page 14, Lines 15-19,19-20 / note the sample or detection system is moved which is a spatial scan).
As to Claim 7,
Chalupczak discloses performing the spatial scan of the measurement location includes moving a primary magnetic field source with respect to the measurement location and/or moving the measurement location with respect to a primary magnetic field source (Page 14, Lines 15-19,19-20 / note the sample or detection system is moved which is a spatial scan).
As to Claim 8,
Chalupczak discloses performing the spatial scan of the measurement location includes detecting an edge and/or curvature of an object in the measurement location and detecting a shape or partial shape of the object (Figure 7 / note this feature reasonably shows the shape / edge of the object).
As to Claim 9,
Chalupczak in view of Girrell discloses classifying the orthogonal component of the secondary magnetic field includes identifying a contribution created by magnetisation and/or a contribution created by eddy currents (Page 8, Line 26 through Page 9, Lines 15 / note any classification, in the combination, must include the identification of the contribution due to a magnetization or eddy currents because it is the magnetization/eddy currents that create the detectable signal).
As to Claim 10,
Chalupczak discloses measuring the orthogonal component of the secondary magnetic field includes measuring an amplitude and optionally a phase thereof (Figure 7), (Page 6, Lines 7-12), (Page 7, Lines 24-29).
As to Claim 11,
Chalupczak discloses measuring the orthogonal component of the secondary magnetic field is performed with an anisotropic sensor configured to have lesser sensitivity to magnetic fields in the first direction, wherein the anisotropic sensor is an atomic magnetometer configured to have lesser sensitivity to magnetic fields in the first direction (Figure 6), (Page 23, Lines 7-12).
As to Claim 12,
Chalupczak discloses the atomic magnetometer is configured with a bias magnetic field axis along the first direction (Page 12, Lines 7-9), (Figure 6 /note the bias magnetic field 26 is along the first direction).
As to Claim 14,
Chalupczak in view of Girrell discloses said at least one reference standard is one or more reference images and/or one or more reference image fragments, and the method includes calculating said similarity to said at least one reference standard using an inverse dependence on amplitude difference (Note: Paragraphs [0022],[0039]-[0041] of Girrell, and that any measured value is reasonably a reference image fragment as such a fragment is merely stored data, and further, in the combination, any calculation to determine a similarity must include an inverse dependence as this is a property of the system), (Claim 15).
As to Claim 15,
Chalupczak in view of Girrell discloses classifying the orthogonal component of the secondary magnetic field is performed by a machine learning algorithm trained on a plurality of reference standards, wherein the plurality of reference standards is a plurality of reference images and/or reference image fragments.
As to Claim 18,
Chalupczak in view of Girrell discloses a system comprising: a computing device (computer) programmed with a program configured to perform, when executed on said computing device as part of said method according to claim 1, a step of classifying the orthogonal component of the secondary magnetic field with reference to a material or material combination type and thereby identifying a type of material or material combination in the measurement location (see the above rejection of Claim 1 as this feature is being interpreted to be the same as that already claimed), a magnetic field source (14) for providing the primary magnetic field; and a magnetic field sensor (atomic magnetometer) for measuring the orthogonal component of the secondary magnetic field (Page 13, Lines 19-27 / note the computer which, in the combination, would reasonably carry out the program).
As to Claim 19,
Chalupczak discloses the magnetic field sensor is configured to provide measurements to the computing device (Figure 6), (Page 23, Lines 7-12).
As to Claim 20,
Chalupczak discloses: a conveyor (2D translation stage) comprising a first section, a second section and a directional change between the first section and the second section (Page 14, Lines 21-24 / note the stage is can move in two dimensions and thus can change the direction of the moving sample, and the sections are the part of the overall system that the stage moves the sample from and two, such as the initial location of the system where the sample is placed onto the stage, to the location of the system where the sample is removed), the conveyor being configured to convey objects from the first section to the second section via the directional change (Page 14, Lines 21-24 / note the stage can move in two directions and thus has a directional change and performs the claim feature); the magnetic field source (14) for providing a primary magnetic field primarily in the first direction into the measurement location located at the directional change of the conveyor (Page 13, Lines 10-27), (Figures 1(a),6); wherein the magnetic field sensor is an anisotropic sensor (atomic magnetometer) configured to have lesser sensitivity to magnetic fields in the first direction and configured to measure an orthogonal component of said secondary magnetic field created by objects at the measurement location (Figure 6), (Page 16, Lines 16-23), (Page 23, Lines 7-12), the orthogonal component of the secondary magnetic field being in a direction substantially orthogonal to the first direction (Page 16, Lines 16-23).
Claims 3 and 4 are rejected under 35 U.S.C. 103 as being unpatentable over Chalupczak et al. (Chalupczak) (GB 2575695 A) in view of Girrell et al. (Girrell) (US 2017/0307500) as applied to Claim 1 and in further view of Bevington et al. (Bevington) (Object surveillance with radio-frequency atomic magnetometers).
As to Claims 3 and 4,
Chalupczak in view of Girrell does not disclose measuring the orthogonal component of the secondary magnetic field includes measuring the orthogonal component of the secondary magnetic field at a plurality of frequencies of the primary magnetic field, the plurality of frequencies includes at least one frequency in each of a first frequency range, a second frequency range, and a third frequency range, first frequency range being no more than 4 kHz, the second frequency range being from 4 kHz to 15 kHz, and the third frequency range being above 15 kHz.
Bevington discloses measuring the orthogonal component of the secondary magnetic field includes measuring the orthogonal component of the secondary magnetic field at a plurality of frequencies of the primary magnetic field, the plurality of frequencies includes at least one frequency in each of a first frequency range, a second frequency range, and a third frequency range, first frequency range being no more than 4 kHz, the second frequency range being from 4 kHz to 15 kHz, and the third frequency range being above 15 kHz (Page 5, Section (IV)(A) / note the use of frequencies 10kHz-60kHz and how different frequency ranges can be used spanning all possible operational frequencies, thus reasonably at least including the above claimed frequency ranges), (or Pages 6-7, Sections (IV)(C)).
It would have been obvious to a person of ordinary skill in the art before the effective filing date to modify Chalupczak in view of Girrell to include measuring the orthogonal component of the secondary magnetic field includes measuring the orthogonal component of the secondary magnetic field at a plurality of frequencies of the primary magnetic field, the plurality of frequencies includes at least one frequency in each of a first frequency range, a second frequency range, and a third frequency range, first frequency range being no more than 4 kHz, the second frequency range being from 4 kHz to 15 kHz, and the third frequency range being above 15 kHz as taught by Bevington in order to advantageously be able to mask and thus remove the impact of the primary magnetic field (Page 5, Section (IV) “Covert” RF Primary Field), and to advantageously be able to detect an object but remove any noise-like harmonic pattern (Page 5, Section (IV)(A)), and in order to advantageously utilize different frequencies that provide additional information about any detected object, thereby providing more information that can be used to classify or otherwise identify the object.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to DAVID M. SCHINDLER whose telephone number is (571)272-2112. The examiner can normally be reached 8am-4:30pm.
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DAVID M. SCHINDLER
Primary Examiner
Art Unit 2858
/DAVID M SCHINDLER/Primary Examiner, Art Unit 2858