DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitation(s) is/are:
“acquiring unit” in claim 1
“extracting unit” in claim 1
“classification unit” in claim 1
“display unit” in claim 1
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
• The specification defines an “acquiring unit” as a microscope in paragraph [0009].
• The specification defines an “extracting unit” as embodied in hardware and/or software in paragraph [0044].
• The specification defines a “classification unit” as embodied in hardware and/or software in paragraph [0044].
• The specification defines a “display unit” as any display including computational circuitry configured to display on a graphical user interface one or more instances of the predicted acne data of keratin material in paragraph [0050].
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
Claims 6-10 and 15-18 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.
Claim 4 recites the limitation " said extracted feature data". There is insufficient antecedent basis for this limitation in the claim. It is recommended the claim be amended to say “said plurality of feature data”. The same issue is present in claims 5 and 14-15.
Claim 6 recites the limitation "a plurality of feature data related to acne among all pores" in line 7. It is unclear if this feature data is the same “plurality of feature data related to acne among all pores” in lines 5-6 of claim 1, or a different set of features. For purposes of examination the claim is being interpreted as “a plurality training of feature data related to acne among all pores”. The same issue is present in claims 7, and 16-17
Claim 9 recites the limitation " said second feature data". There is insufficient antecedent basis for this limitation in the claim. It is recommended the claim be amended to say “said second set of feature data”.
Claims not explicitly rejected above are rejected because they depend from claims rejected above as indefinite.
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.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. The claim(s) as a whole, considering all claim elements both individually and in combination, do not amount to significantly more than an abstract idea. A streamlined analysis of claim 11 follows.
STEP 1
Regarding claim 11, the claim recites a series of steps or acts, including classifying extracted feature data. Thus, the claim is directed to a process, which is one of the statutory categories of invention.
STEP 2A, PRONG ONE
The claim is then analyzed to determine whether it is directed to any judicial exception. The step of classifying extracted feature data sets forth a judicial exception. This step describes a concept performed in the human mind (including an observation, evaluation, judgment, opinion). Thus, the claim is drawn to a Mental Process/Mathematical Concept, which is an Abstract Idea.
STEP 2A, PRONG TWO
Next, the claim as a whole is analyzed to determine whether the claim recites additional elements that integrate the judicial exception into a practical application. The claim fails to recite an additional element or a combination of additional elements to apply, rely on, or use the judicial exception in a manner that imposes a meaningful limitation on the judicial exception. Claim 11 recites displaying said classified results; wherein said classified results are acne proneness information or acne frequency information, which is merely adding insignificant extra-solution activity to the judicial exception (MPEP 2106.05(g)). The display of the classified results does not provide an improvement to the technological field, the method does not effect a particular treatment or effect a particular change based on the displayed results, nor does the method use a particular machine to perform the Abstract Idea.
STEP 2B
Next, the claim as a whole is analyzed to determine whether any element, or combination of elements, is sufficient to ensure that the claim amounts to significantly more than the exception. The claim also recites the step of “extracting a plurality of feature data related to acne among all pores from one or more digital images of said region of keratin material of the human subject” which is an abstract idea in the form of a mental process. Besides the Abstract Ideas, the claim recites additional steps of acquiring one or more digital images of a region of keratin material of the human subject. Obtaining image data is well-understood, routine and conventional activity for those in the field of medical diagnostics. Further, the acquiring step is recited at a high level of generality such that it amounts to insignificant presolution activity, e.g., mere data gathering step necessary to perform the Abstract Idea. When recited at this high level of generality, there is no meaningful limitation, such as a particular or unconventional step that distinguishes it from well-understood, routine, and conventional data gathering and comparing activity engaged in by medical professionals prior to Applicant's invention. Furthermore, it is well established that the mere physical or tangible nature of additional elements such as the obtaining and comparing steps do not automatically confer eligibility on a claim directed to an abstract idea (see, e.g., Alice Corp. v. CLS Bank Int'l, 134 S.Ct. 2347, 2358-59 (2014)).
Consideration of the additional elements as a combination also adds no other meaningful limitations to the exception not already present when the elements are considered separately. Unlike the eligible claim in Diehr in which the elements limiting the exception are individually conventional, but taken together act in concert to improve a technical field, the claim here does not provide an improvement to the technical field. Even when viewed as a combination, the additional elements fail to transform the exception into a patent-eligible application of that exception. Thus, the claim as a whole does not amount to significantly more than the exception itself. The claim is therefore drawn to non-statutory subject matter.
Regarding claim 1, the device recited in the claim is a generic device comprising generic components configured to perform the abstract idea. The recited acquiring unit is a generic device configured to perform pre-solutional data gathering activity, the display unit is a generic device configured to perform result displaying, and the computing device is configured to perform the Abstract Idea. According to section 2106.05(f) of the MPEP, merely using a computer as a tool to perform an abstract idea does not integrate the Abstract Idea into a practical application.
The dependent claims also fail to add something more to the abstract independent claims as they generally recite method steps pertaining to the abstract ideas and data acquisition/storage. Claims 19 and 20 merely recite tools to perform the method. Claims 2-3 and 12-13 are mere presoulutional data gathering. Claims 4-10 recite details regarding classification and machine learning model training which are abstract ideas in the form of mental processes/mathematical concepts The classifying and extracting steps recited in the independent claims maintain a high level of generality when considered in combination with the dependent claims.
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.
Claim(s) 1-5, 11-15 and 19-20 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Bandic (US 20100185064 A1 -cited by applicant).
In regards to claim 1 Bandic teaches a computing device for predicting acne properties for keratin material of a human object, the computing device comprising ([0386 “Hardware 108, 109 may comprise an imaging device that may connect with a computer, online platform 120, mobile platform 124, and the like via the user interface 102 and enable users to capture an image that enables measure various skin health, condition and type parameters.”):
an acquiring unit including computational circuitry configured to acquire one or more digital images of a region of keratin material of the human subject ([0044] “According to a second aspect, a system for skin phototype determination using photograph analysis may be disclosed. The system may include an image capturing device for capturing digital images of a skin”);
an extracting unit including computational circuitry configured to extract a plurality of feature data related to acne among all pores from said one or more digital images of said region of keratin material of the human subject ([0100] “According to an aspect, the system for determining a predisposition of sebaceous pores and skin structures around a sebaceous gland, a level of acne, and a predisposition of a portion of skin to improve and worsen the acne wherein the optical assessment unit is coupled to both the image capturing device and the image processor”, [0102] “The method for determining a predisposition of sebaceous pores and skin structures around a sebaceous gland, a level of acne, and a predisposition of a portion of skin to improve and worsen the acne may include analyzing at least one of an individual level of pores, glands, inflammation, sebum, blockage and age of sebum over a surface area, an aggregate level of pores, glands, inflammation, sebum, blockage and age of sebum over a surface area and a correlation level of pores, glands, inflammation, sebum, blockage and age of sebum over a surface area. The correlation level may include at least one of a fuzzy logic, a non-linear regression, a genetic algorithm, and a neural network”);
a classification unit including computational circuitry configured to classify said extracted feature data ([0312] “In an embodiment, the algorithm 150 may be based on artificial neural networks, non-linear regression, or fuzzy logic. For example, the algorithm 150 may be used in skin lesion diagnosis based on a probabilistic framework for classification”);
and a display unit including computational circuitry configured to display said classified results ([0258] “In some embodiments, the skin condition assessment data of locations may be overlaid on an image of a larger skin region displayed on the display surface, providing a useful picture of the health of the entire skin region in a single view”);
wherein said classified results are acne proneness information or acne frequency information ([0102] “a level of acne” is acne proneness and “a predisposition of a portion of skin to improve and worsen the acne” is acne frequency information).
In regards to claim 2 Bandic teaches the computing device of claim 1, wherein the computing device also further comprises data storage to store said digital images ([0235] “Data may be stored in an internal device memory 168”).
In regards to claim 3 Bandic teaches the computing device of claim 1, wherein said acquiring unit is embodied by a microscope ([0491] “In certain other embodiments, a comparative analysis of pictures of materials captured by classical optical microscopy and OMF has been discussed”).
In regards to claim 4 Bandic teaches the computing device of claim 1, wherein said classification unit includes computational circuitry configured to classify a first set of feature data from said extracted feature data by a first machine learning model to obtain a result that said classified results are acne proneness information ([0311] Algorithms 150” multiple algorithms, [102] “a level of acne” is acne proneness).
In regards to claim 5 Bandic teaches the computing device of claim 4, wherein said classification unit includes computational circuitry configured to classify a second set of feature data from said extracted feature data by a second machine learning model to obtain a result that said classified results are acne frequency information ([0311] Algorithms 150” multiple algorithms, [0102] “a predisposition of a portion of skin to improve and worsen the acne” is acne frequency information).
In regards to claim 11 Bandic teaches a method for predicting acne properties for keratin material of a human object, the method comprising:
acquiring one or more digital images of a region of keratin material of the human subject ([0044] “According to a second aspect, a system for skin phototype determination using photograph analysis may be disclosed. The system may include an image capturing device for capturing digital images of a skin”);
extracting a plurality of feature data related to acne among all pores from one or more digital images of said region of keratin material of the human subject ([0100] “According to an aspect, the system for determining a predisposition of sebaceous pores and skin structures around a sebaceous gland, a level of acne, and a predisposition of a portion of skin to improve and worsen the acne wherein the optical assessment unit is coupled to both the image capturing device and the image processor”, [0102] “The method for determining a predisposition of sebaceous pores and skin structures around a sebaceous gland, a level of acne, and a predisposition of a portion of skin to improve and worsen the acne may include analyzing at least one of an individual level of pores, glands, inflammation, sebum, blockage and age of sebum over a surface area, an aggregate level of pores, glands, inflammation, sebum, blockage and age of sebum over a surface area and a correlation level of pores, glands, inflammation, sebum, blockage and age of sebum over a surface area. The correlation level may include at least one of a fuzzy logic, a non-linear regression, a genetic algorithm, and a neural network”);
classifying said extracted feature data ([0312] “In an embodiment, the algorithm 150 may be based on artificial neural networks, non-linear regression, or fuzzy logic. For example, the algorithm 150 may be used in skin lesion diagnosis based on a probabilistic framework for classification”);
and displaying said classified results ([0258] “In some embodiments, the skin condition assessment data of locations may be overlaid on an image of a larger skin region displayed on the display surface, providing a useful picture of the health of the entire skin region in a single view”);
wherein said classified results are acne proneness information or acne frequency information ([0102] “a level of acne” is acne proneness and “a predisposition of a portion of skin to improve and worsen the acne” is acne frequency information).
In regards to claim 12 Bandic teaches the method of claim 11, further comprising said method also comprises storing said digital images ([0235] “Data may be stored in an internal device memory 168”.
In regards to claim 13 Bandic teaches the method of claim 11, wherein said acquiring one or more digital images of a region of keratin material of the human subject is embodied by a microscope ([0491] “In certain other embodiments, a comparative analysis of pictures of materials captured by classical optical microscopy and OMF has been discussed”).
In regards to claim 14 Bandic teaches the method of claim 11, wherein classifying said extracted feature data also further comprises classifying a first set of feature data from said extracted feature data by a first machine learning model to obtain a result that said classified results are acne proneness information ([0311] Algorithms 150” multiple algorithms, [102] “a level of acne” is acne proneness).
In regards to claim 15 Bandic teaches the method of claim 14, wherein classifying said extracted feature data also further comprises classifying a second set of feature data from said extracted feature data by a second machine learning model to obtain a result that said classified results are acne frequency information ([0311] Algorithms 150” multiple algorithms, [0102] “a predisposition of a portion of skin to improve and worsen the acne” is acne frequency information.
In regards to claim 19 Bandic teaches a computer readable medium having stored thereon instructions that when executed cause a computing device to perform the method according to claim 11 ([0458] The host computing subsystem 7200 may comprise a processing unit 7202, a memory unit 7204 and an Input/Output (or I/O) unit 7206 respectively).
In regards to claim 20 Bandic teaches an apparatus for predicting acne properties for keratin material of a human object, the apparatus comprises means for performing the method according to claim 11 ([0386 “Hardware 108, 109 may comprise an imaging device that may connect with a computer, online platform 120, mobile platform 124, and the like via the user interface 102 and enable users to capture an image that enables measure various skin health, condition and type parameters.”).
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.
Claim(s) 6-7 and 16-17 is/are rejected under 35 U.S.C. 103 as being unpatentable over Bandic (US 20100185064 A1 -cited by applicant) as applied to claims 4-5 and 14-15, further in view of Flank (US 20190220738 A1).
In regards to claim 6 Bandic teaches the computing device of claim 4, Bandic fails to teach how the algorithms are trained.
Flank teaches a method of training a machine learning model is trained by the following actions:
obtaining clinical assessment data related to acne for a plurality of sampled human objects ([0034] "The images can include a variety of skin conditions, including, but not limited to, herpes, acne, melanomas, and poison ivy”, known conditions are clinical assessment data);
obtaining self-claim data of acne history of said plurality of sampled human objects ([0021] “Moreover, disclosed embodiments, through continued training, can continuously improve the performance and personalization of this network by feeding it additional information (e.g. medical history of the patient, preexisting conditions, etc.”));
acquiring one or more digital images of a region of keratin material of said plurality of sampled human objects ([0034] “At 302, a training image set is obtained. This can include multiple digital images in a variety of formats, including, but not limited to, JPEG, PNG, bitmap, TIFF, Targa, or another suitable format. The images can include a variety of skin conditions, including, but not limited to, herpes, acne, melanomas, and poison ivy”);
extracting a plurality of feature data related to acne among all pores from one or more digital images of said region of keratin material of said plurality of sampled human subjects ([0034] "The images can include a variety of skin conditions, including, but not limited to, herpes, acne, melanomas, and poison ivy. The images may then be preprocessed at process 306. The preprocessing can include, but is not limited to, resizing, contrast adjustment, noise removal, edge detection, gradient detection, color adjustments, and/or other suitable image processing techniques", processed image is feature);
and building said first machine learning model based on by using said clinical assessment data, said self-claim data of acne history and a first set of feature data from said extracted feature data ([0021] “Moreover, disclosed embodiments, through continued training, can continuously improve the performance and personalization of this network by feeding it additional information (e.g. medical history of the patient, preexisting conditions, etc.”, [0034] “At 308, the processed training images are then used to train machine learning skin condition analysis module”).
It would have been prima facie obvious to person of ordinary skill in the art to train the neural networks of Beric using the training method of Flank and data relating to different levels of acne proneness. Doing so would merely be combining prior art elements according to known methods to yield the predictable result of training the neural network to accurately detect acne proneness of an individual.
In regards to claim 7 Bandic teaches the computing device of claim 5, Bandic fails to teach how the algorithms are trained.
Flank teaches a method of training a machine learning model is trained by the following actions:
obtaining clinical assessment data related to acne for a plurality of sampled human objects ([0034] "The images can include a variety of skin conditions, including, but not limited to, herpes, acne, melanomas, and poison ivy”, known conditions are clinical assessment data);
obtaining self-claim data of acne history of said plurality of sampled human objects ([0021] “Moreover, disclosed embodiments, through continued training, can continuously improve the performance and personalization of this network by feeding it additional information (e.g. medical history of the patient, preexisting conditions, etc.”));
acquiring one or more digital images of a region of keratin material of said plurality of sampled human objects ([0034] “At 302, a training image set is obtained. This can include multiple digital images in a variety of formats, including, but not limited to, JPEG, PNG, bitmap, TIFF, Targa, or another suitable format. The images can include a variety of skin conditions, including, but not limited to, herpes, acne, melanomas, and poison ivy”);
extracting a plurality of feature data related to acne among all pores from one or more digital images of said region of keratin material of said plurality of sampled human subjects ([0034] "The images can include a variety of skin conditions, including, but not limited to, herpes, acne, melanomas, and poison ivy. The images may then be preprocessed at process 306. The preprocessing can include, but is not limited to, resizing, contrast adjustment, noise removal, edge detection, gradient detection, color adjustments, and/or other suitable image processing techniques", processed image is feature);
It would have been prima facie obvious to person of ordinary skill in the art to train the neural networks of Beric using the training method of Flank and data relating to different levels of acne frequency. Doing so would merely be combining prior art elements according to known methods to yield the predictable result of training the neural network to accurately detect acne frequency of an individual.
In regards to claim 16 Bandic teaches the method of claim 14. Bandic fails to teach how the algorithms are trained.
Flank teaches a method of training a machine learning model is trained by the following actions:
obtaining clinical assessment data related to acne for a plurality of sampled human objects ([0034] "The images can include a variety of skin conditions, including, but not limited to, herpes, acne, melanomas, and poison ivy”, known conditions are clinical assessment data);
obtaining self-claim data of acne history of said plurality of sampled human objects ([0021] “Moreover, disclosed embodiments, through continued training, can continuously improve the performance and personalization of this network by feeding it additional information (e.g. medical history of the patient, preexisting conditions, etc.”));
acquiring one or more digital images of a region of keratin material of said plurality of sampled human objects ([0034] “At 302, a training image set is obtained. This can include multiple digital images in a variety of formats, including, but not limited to, JPEG, PNG, bitmap, TIFF, Targa, or another suitable format. The images can include a variety of skin conditions, including, but not limited to, herpes, acne, melanomas, and poison ivy”);
extracting a plurality of feature data related to acne among all pores from one or more digital images of said region of keratin material of said plurality of sampled human subjects ([0034] "The images can include a variety of skin conditions, including, but not limited to, herpes, acne, melanomas, and poison ivy. The images may then be preprocessed at process 306. The preprocessing can include, but is not limited to, resizing, contrast adjustment, noise removal, edge detection, gradient detection, color adjustments, and/or other suitable image processing techniques", processed image is feature);
and building said first machine learning model based on by using said clinical assessment data, said self-claim data of acne history and a first set of feature data from said extracted feature data ([0021] “Moreover, disclosed embodiments, through continued training, can continuously improve the performance and personalization of this network by feeding it additional information (e.g. medical history of the patient, preexisting conditions, etc.”, [0034] “At 308, the processed training images are then used to train machine learning skin condition analysis module”).
It would have been prima facie obvious to person of ordinary skill in the art to train the neural networks of Beric using the training method of Flank and data relating to different levels of acne proneness. Doing so would merely be combining prior art elements according to known methods to yield the predictable result of training the neural network to accurately detect acne proneness of an individual.
In regards to claim 17 Bandic teaches the method of claim 15, Bandic fails to teach how the algorithms are trained.
Flank teaches a method of training a machine learning model is trained by the following actions:
obtaining clinical assessment data related to acne for a plurality of sampled human objects ([0034] "The images can include a variety of skin conditions, including, but not limited to, herpes, acne, melanomas, and poison ivy”, known conditions are clinical assessment data);
obtaining self-claim data of acne history of said plurality of sampled human objects ([0021] “Moreover, disclosed embodiments, through continued training, can continuously improve the performance and personalization of this network by feeding it additional information (e.g. medical history of the patient, preexisting conditions, etc.”));
acquiring one or more digital images of a region of keratin material of said plurality of sampled human objects ([0034] “At 302, a training image set is obtained. This can include multiple digital images in a variety of formats, including, but not limited to, JPEG, PNG, bitmap, TIFF, Targa, or another suitable format. The images can include a variety of skin conditions, including, but not limited to, herpes, acne, melanomas, and poison ivy”);
extracting a plurality of feature data related to acne among all pores from one or more digital images of said region of keratin material of said plurality of sampled human subjects ([0034] "The images can include a variety of skin conditions, including, but not limited to, herpes, acne, melanomas, and poison ivy. The images may then be preprocessed at process 306. The preprocessing can include, but is not limited to, resizing, contrast adjustment, noise removal, edge detection, gradient detection, color adjustments, and/or other suitable image processing techniques", processed image is feature);
and building said first machine learning model based on by using said clinical assessment data, said self-claim data of acne history and a first set of feature data from said extracted feature data ([0021] “Moreover, disclosed embodiments, through continued training, can continuously improve the performance and personalization of this network by feeding it additional information (e.g. medical history of the patient, preexisting conditions, etc.”, [0034] “At 308, the processed training images are then used to train machine learning skin condition analysis module”).
It would have been prima facie obvious to person of ordinary skill in the art to train modify the neural networks of Beric using the training method of Flank and data relating to different levels of acne frequency. Doing so would merely be combining prior art elements according to known methods to yield the predictable result of training the neural network to accurately detect acne frequency of an individual.
Examiner’s Note
In regards to claims 8, 10, and 18 None of the prior art teaches or suggests, either alone or in combination, a device or method comprising a ratio of hyper-keratinization follicles being used as a feature to determine acne information, in combination with the other claimed elements/steps.
In regards to claims 9, None of the prior art teaches or suggests, either alone or in combination, a device comprising a ratio of hyper-keratinization follicles, a ratio of follicles with thick keratinized border and a ratio of follicles with inner keratin content being used as a feature to determine acne information, in combination with the other claimed elements.
Claims 8-10 and 18 contain no prior art rejections, however they are not in condition for
allowance due to their rejections under 35 U.S.C. 101 and/or 35 U.S.C. 112(b).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to LUCY EPPERT whose telephone number is (571)270-0818. The examiner can normally be reached M-F 7:30-5:00 EST.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Jennifer Robertson can be reached at (571) 272-5001. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/LUCY EPPERT/Examiner, Art Unit 3791
/ADAM J EISEMAN/Primary Examiner, Art Unit 3791
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