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 .
Drawings
The drawings are objected to because Figs. 7 and 9 are poor quality reproductions that do not have satisfactory reproduction characteristics contrary to 37 CFR 1.84(l). Both of these figures including drawings that have been reduced in size to much such that the fonts are too small and text unreadable. Corrected drawing sheets in compliance with 37 CFR 1.121(d) are required in reply to the Office action to avoid abandonment of the application. Any amended replacement drawing sheet should include all of the figures appearing on the immediate prior version of the sheet, even if only one figure is being amended. The figure or figure number of an amended drawing should not be labeled as “amended.” If a drawing figure is to be canceled, the appropriate figure must be removed from the replacement sheet, and where necessary, the remaining figures must be renumbered and appropriate changes made to the brief description of the several views of the drawings for consistency. Additional replacement sheets may be necessary to show the renumbering of the remaining figures. Each drawing sheet submitted after the filing date of an application must be labeled in the top margin as either “Replacement Sheet” or “New Sheet” pursuant to 37 CFR 1.121(d). If the changes are not accepted by the examiner, the applicant will be notified and informed of any required corrective action in the next Office action. The objection to the drawings will not be held in abeyance.
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitation(s) is/are:
Cervical cancer diagnosis camera device which is comprised of a camera unit, first reading unit, touch panel unit, and communication unit and server which is comprised of a data generation unit, a learning unit, a second reading unit, a read request unit and a result providing unit, local server and central server and payment processing unit in claims 1-8. It is noted the “device” of the cervical cancer diagnosis camera device is suggestive of structure but upon closer examination this “device” is a collection of functionally defined units and that actual structure or terms recognized as structure to those of ordinary skill in the art are absent from the claims. Likewise, the “Server” is also composed of functionally defined “units”.
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 3 and 4 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 3 recites “wherein the first reading unit (120) predicts and outputs the AI reading results in an embedded state”. The term “embedded state is not understood and not define in the instant specification. This term implies the internal state of the AI model which is contrary to any output of such an internal state and the conventional understanding of a neural network.
Claim 4 recites “the cervical cancer diagnosis camera device (100) comprises a controller (150) configured to terminate a test when the AI reading result output from the first reading unit (120) has the highest negative probability and matches an opinion of the user, and transmit the read request to the server (200) through the communication unit (140) if the AI reading result output from the first reading unit (120) has the highest positive probability or differs from the opinion of the user”
The highlighted terms in claim 4 above are relative terms any one of which renders the claim indefinite. These terms have not been defined by the claim, the specification does not provide a standard for ascertaining the requisite degree, and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention. Furthermore, it is unclear how these highest and (positive or negative) numerical probabilities are compared to the opinion of the user to determine “matches” and “differ[ing]” opinions.
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.
Claims 1-7 are rejected under 35 U.S.C. 103 as being unpatentable over Xue {Xue, Peng, Man Tat Alexander Ng, and Youlin Qiao. "The challenges of colposcopy for cervical cancer screening in LMICs and solutions by artificial intelligence." BMC medicine 18.1 (2020): 169}; Vajinepalli (WO2013/084123 A2); and Xue2 {Xue, Peng, et al. "Development and validation of an artificial intelligence system for grading colposcopic impressions and guiding biopsies." BMC medicine 18.1 (2020): 406 and associated Supplementary Materials}.
Claim 1
In regards to claim 1, Xue discloses an artificial intelligence-based cervical cancer screening service system {see Fig. 1 (copied below) clearly illustrating such a system described as AI-guided colposcopy as an auxiliary diagnostic tool for LMICs (low- and middle-income countries as further explained below}, the cervical cancer screening service system comprising:
a cervical cancer diagnosis camera device (100) which is equipped with a first read model pre-trained to read cervical images, and is configured to capture images of a cervix and output AI reading results based on the captured cervical images as an input of the first read model {see Fig. 1 including, on left side, a colposcope having a camera capturing cervigrams (digital cervical images are more commonly referred to as “cervigrams” and an AI local server with a first pre-trained model that outputs AI reading results (diagnoses). See also Background, The Solutions to Development and Application of AI-guided digital colposcopy and The Advancement sections discussing conventional AI models for cervical image screening/diagnosis that may be used}; and
a server (200) configured to receive a read request including the cervical images from the cervical cancer diagnosis camera device (100) {Fig. 1 including AI Cloud that receives a “read request” including the captured cervigrams},
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request a reading specialist to read the cervical images, and provide a final reading report to a user of the cervical cancer diagnosis camera device (100) {The AI cloud may be considered a “reading specialist” that reads the cervigrams and provides a final reading report (diagnosis) to the client device. Xue also discloses that, particularly for rare cervical diseases or complex complications, a human colposcopiists should continue to be responsible for the final result and to provide feedback and training for human colposcopists to improve their own diagnostic ability by reviewing and confirming the AI-based diagnosis},
wherein the cervical cancer diagnosis camera device (100) comprises:
a camera unit (110) configured to capture the cervical images by photographing the cervix {see above ;
a first reading unit (120) configured to store the first read model, receive the cervical images captured by the camera unit (110), then predict and output the Al reading results from the first read model {See above while noting, in particular, that Xue specifically discloses a benefit of AI which is reporting the diagnostic results in real time “therefore, the integration of the AI algorithm into colposcopy equipment could help colposcopists to improve clinical workflow in a busy colposcopy clinic”};
a communication unit (140) configured to transmit a read request including the cervical images to the server (200) {see above wherein the cervigrams are transmitted to the AI cloud upon request of the user such that the cloud server can read/diagnose based on the images}.
Although Xue reaffirms the role of a human colposcopists such that the human doctor can integrate other information such as complex complications and/or knowledge of rare cervical diseases such that the AI model diagnostic output is used a reference with the human continuing to be responsible for the final diagnosis (result) and although Xue also outputs real-time images captured by the output unit, Xue does not specifically mention the use of wholly conventional touch panel to “receive an input signal from the user”.
Vajinepalli is a highly analogous reference from the same field of cervical image cancer diagnosis. See abstract and Figs. 1-3 including colposcope with camera imaging a cervix, an automated colposcopic examination algorithm and a display.
also demonstrates the conventional nature of using a touch screen (a touch panel unit) configured to output real-time images captured by the camera unit (110) and the reading results of the automated colposcopic examination on a first reading unit (display), and receive an input signal from the user {see Figs. 1, 18, 19, and 22, pg. 17, lines 13-26; pg. 24, lines 1-7, pg. 26, lines 1-27, pg. 27, lines 20-34, pg. 28, lines 23-pg. 29, line 21, pg. 30, lines 1-17 including an expert receiving the cervical images on mobile phone and using the touch screen to annotate and mark the lesions from the automated examination.
It 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 to have modified Xue which already reaffirms the role of a human colposcopists such that the human doctor can integrate other information such as complex complications and/or knowledge of rare cervical diseases such that the AI model diagnostic output is used a reference with the human continuing to be responsible for the final diagnosis (result) and also discloses outputting real-time images captured by the output unit such that the output display includes a wholly conventional touch panel to “receive an input signal from the user” as taught by Vajinepalli because touch screens provides a convenient and mobile user interface thereby increasing the availability and throughput of human doctors while also leveraging the diagnostic capabilities of AI, because there is a reasonable expectation of success and/or because doing so merely combines prior art elements according to known methods to yield predictable results.
Although Xue discloses an artificial intelligence-based cervical cancer screening service system including client (cervical cancer diagnosis camera device) and server in which both the client implement AI models providing cervical cancer diagnoses, Xue does not specifically describe such AI models.
Xue2 a highly analogous reference from the same field of cervical image cancer diagnosis. See title, abstract and cites below.
Xue2 teaches AI models specifically developed for diagnosing cervical cancer based on cervical images. See abstract, Methods, Fig. 1 and Development of the CAIA DS (Colposcopic Artificial Intelligence Auxillary Diagnostic System), and Supplementary Materials. Xue2 also applies these AI models at the client level and/or server level as shown in Fig. 1.
It 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 to have modified Xue which already discloses an artificial intelligence-based cervical cancer screening service system including client (cervical cancer diagnosis camera device) and server in which both the client implement AI models providing cervical cancer diagnoses such that this system includes operable AI models for the client and server as taught by Xue2 because providing working AI models greatly increases the usefulness of the system outlined by Xue, because Xue2 is a complementary paper by the same author penned to provide such details thus making the combined system not only highly predictable but also providing validated test data showing the combination working together successfully with a low diagnostic error rate, because there is not only a reasonable expectation of success but actual proven success for combining Xue2 with Xue, and/or because doing so merely combines prior art elements according to known methods to achieve proven results.
Claim 2
In regards to claim 2, Xue is not relied upon to disclose but Xue2 teaches wherein the server (200) comprises:
a data generation unit (210) configured to detect a cervical region in the cervical images classified according to lesion criteria, and generate and store annotation data {see above including Fig. 1 and Supplemental Methods including section a Cervix Detection and section c Feature Fusion Network that classifies the detected cervical region into lesion criteria (e.g. normal/benign, low-grade, high-grade, and cancer) and store annotation data such detected classification and lesion area segmentation};
a learning unit (220) configured to train a second read model based on deep learning so as to predict the reading results by understanding a relationship between a cervical region image in the cervical images and the lesion criterion using the annotation data as training data {see Fig. 1 including training the model using hand-annotated images (training set), which may also be tuned and validated using additional deep learning of the tuning set and validation set to develop the AI model to predict the reading results by understanding a relationship between a cervical region image in the cervical images and the lesion criterion using the annotation data as training data. See also Supplemental Info including Training Details and Semi-Supervised Learning Using Tuning Set, Figure S2 Cervix image annotation tool and Figures S3-S4};
a second reading unit (230) configured to output the AI reading results based on artificial intelligence by applying the cervical images received from the cervical cancer diagnosis camera device (100) to the second read model trained by the learning unit (220) {see above including Fig. 1 in which the CAIA DS AI model may be deployed and used to output AI reading results from client (AI local server)/cervical cancer diagnosis camera device and/or server (AI cloud platform);
a read request unit (240) configured to transmit the cervical images and the Al reading results of the second reading unit (230) to the reading specialist to request a final reading; and a result providing unit (250) configured to receive the final reading report from the reading specialist and provide the report to the user of the cervical cancer diagnosis camera device (100) {see above wherein the cervigrams are transmitted to the AI cloud and/or local server upon request of the user such that the cloud server can read/diagnose based on the images the result of which are sent back to the client for display and evaluation by a reading specialist who makes the final diagnosis (final reading) who reports to the user}.
It 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 to have modified Xue which already discloses the framework for both client-side and/or server-side cervical cancer diagnosis based on cervical images using respective AI models such that the system includes the data generation unit, learning unit, second reading unit, read request unit and result providing unit of claim 2 as taught by Xue2 because providing working AI models greatly increases the usefulness of the system outlined by Xue, because Xue2 is a complementary paper by the same author penned to provide such details thus making the combined system not only highly predictable but also providing validated test data showing the combination working together successfully with a low diagnostic error rate, because there is not only a reasonable expectation of success but actual proven success for combining Xue2 with Xue, and/or because doing so merely combines prior art elements according to known methods to achieve proven results.
Claim 3
In regards to claim 3, Xue discloses wherein the first reading unit (120) predicts and outputs the AI reading results in an embedded state {see the 112b rejection above. To the extent understood the AI models employed by Xue outputs the AI reading results in an embedded state.
Claim 4
In regards to claim 4, Xue is not relied upon to disclose but Xue2 teaches
wherein the first reading unit (120) outputs negative, positive and possibility of needing a biopsy as a probability, respectively {see above including Fig. 1 and Supplemental Methods including section a Cervix Detection and section c Feature Fusion Network that classifies the detected cervical region into lesion criteria (e.g. normal/benign, low-grade, high-grade, and cancer. See also biopsy site guiding outputs which also relate to possibility of needing a biopsy}, and
the cervical cancer diagnosis camera device (100) comprises a controller (150) configured to terminate a test when the AI reading result output from the first reading unit (120) has the highest negative probability and matches an opinion of the user, and transmit the read request to the server (200) through the communication unit (140) if the AI reading result output from the first reading unit (120) has the highest positive probability or differs from the opinion of the user {see the 112b rejection. Also, initially it is noted that “terminate a test” does not specify what test is being terminated thus broadening the claim scope to include situations in which the AI reading result output is “cancer” and matches the human diagnosis of cancer in which case system outputs the result or, if different, the AI model is used to confirm or provide a deeper level diagnosis. As to controller, see the implementation details in the supplemental materials which include a processor}.
It 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 to have modified Xue which already discloses the framework for both client-side and/or server-side cervical cancer diagnosis based on cervical images using respective AI models such that the system includes the first reading unit (120) and the cervical cancer diagnosis camera device (100) comprises a controller (150) configured to terminate a test when the AI reading result output from the first reading unit (120) has the highest negative probability and matches an opinion of the user, and transmit the read request to the server (200) through the communication unit (140) if the AI reading result output from the first reading unit (120) has the highest positive probability or differs from the opinion of the user as taught by Xue2 because providing working AI models greatly increases the usefulness of the system outlined by Xue, because Xue2 is a complementary paper by the same author penned to provide such details thus making the combined system not only highly predictable but also providing validated test data showing the combination working together successfully with a low diagnostic error rate, because there is not only a reasonable expectation of success but actual proven success for combining Xue2 with Xue, and/or because doing so merely combines prior art elements according to known methods to achieve proven results.
Claim 5
In regards to claim 5, Xue is not relied upon to disclose but Xue2 teaches wherein the first read model and the second read model comprise:
a detection model configured to detect the cervical region in the cervical images {See Supplemental Methods including section a Cervix detection}; and
a classification model configured to predict the Al reading results from the cervical region detected by the detection model {see Supplemental Methods including section c Feature fusion network that precuts the AI reading results from the detected cervical region. See also section d Lesion area segmentation network}.
It 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 to have modified Xue which already discloses the framework for both client-side and/or server-side cervical cancer diagnosis based on cervical images using respective AI models such that the system includes a detection model configured to detect the cervical region in the cervical images; and a classification model configured to predict the Al reading results from the cervical region detected by the detection model
as taught by Xue2 because providing working AI models greatly increases the usefulness of the system outlined by Xue, because Xue2 is a complementary paper by the same author penned to provide such details thus making the combined system not only highly predictable but also providing validated test data showing the combination working together successfully with a low diagnostic error rate, because there is not only a reasonable expectation of success but actual proven success for combining Xue2 with Xue, and/or because doing so merely combines prior art elements according to known methods to achieve proven results.
Claim 6
In regards to claim 6, Xue discloses.
a user device (300) of the user who performs cervical cancer screening using the cervical cancer diagnosis camera device (100), wherein the result providing unit (250) provides the final reading report through an application or web program installed in the user device (300), and the communication unit 140 transmits the cervical images to the application installed in the user device (300).
{See above while noting, in particular, that Xue specifically discloses a benefit of AI which is reporting the diagnostic results in real time “therefore, the integration of the AI algorithm into colposcopy equipment could help colposcopists to improve clinical workflow in a busy colposcopy clinic” including transmitting the cervical images to the client device (user device) to an application in the user device such that the images and AI output results may be confirmed by the human user of the user device.}
Claim 7
In regards to claim 7, Xue is not relied upon to disclose but Xue2 teaches wherein the server (200) comprises a central server configured to provide the final reading report according to the read request and a plurality of local servers located in preset local bases, wherein the communication unit (140) of the cervical cancer diagnosis camera device (100) transmits the read request to a local server of the nearest base among the local servers, and the local server that has received the read request transmits the read request to the central server, then receives and provides the final read report according to the read request {see cloud server. See also Supplemental Materials including Multi-center domain adaptation in which plural local servers located in preset local bases are used in which these respective client devices (local servers) may transmit read requests to central server (cloud server). As to “nearest base”, note that this term is broad. Note also that the method does not actively determine which is the “nearest” base among the local servers. Still further, the requesting client device of Xue2 is considered a “nearest base” because it is co-located at the respective clinic.}
It 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 to have modified Xue which already discloses the framework for both client-side and/or server-side cervical cancer diagnosis based on cervical images using respective AI models such that the system includes a central server configured to provide the final reading report according to the read request and a plurality of local servers located in preset local bases, wherein the communication unit (140) of the cervical cancer diagnosis camera device (100) transmits the read request to a local server of the nearest base among the local servers, and the local server that has received the read request transmits the read request to the central server, then receives and provides the final read report according to the read request as taught by Xue2 because providing working AI models greatly increases the usefulness of the system outlined by Xue, because Xue2 is a complementary paper by the same author penned to provide such details thus making the combined system not only highly predictable but also providing validated test data showing the combination working together successfully with a low diagnostic error rate, because there is not only a reasonable expectation of success but actual proven success for combining Xue2 with Xue, and/or because doing so merely combines prior art elements according to known methods to achieve proven results.
Claim 8 is rejected under 35 U.S.C. 103 as being unpatentable over Xue, Vajinepalli, and Xue2 as applied to claim 7 above, and further in view of Wahlstrom (US 2019/0007849 A1).
Claim 8
In regards to claim 8, Xue is not relied upon to disclose the payment processing of claim 8.
Wahlstrom is analogous art from the same field of automated diagnostics using AI models. See abstract, [0003], [0024], [0173] including client/server architectures as per Figs. 1-7.
Wahlstrom also teaches the highly conventional nature of requiring payment for services rendered including a payment processing unit (260) configured to manage points of the user and deduct the points when receiving the read request from the cervical cancer diagnosis camera device (100) {see [0054], [0067], [0072] including pricing data store 304 to charges a price, manage and deducts points (money) upon requests for services/content}.
It 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 to have modified Xue which already discloses the framework for both client-side and/or server-side cervical cancer diagnosis based on cervical images using respective AI models such that the system includes a payment processing unit (260) configured to manage points of the user and deduct the points when receiving the read request from the cervical cancer diagnosis camera device as taught by Wahlstrom because doing so enables fair monetary compensation for providing diagnostic services using an AI model, because AI models are notoriously expensive to build, train and operate such that providing compensation is necessary or otherwise advantageous to the continued use and proliferation of life-saving medical diagnostics, because there is a reasonable expectation of success and/or because doing so merely combines prior art elements according to known methods to yield predictable results.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Michael R Cammarata whose telephone number is (571)272-0113. The examiner can normally be reached M-Th 7am-5pm EST.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Matthew Bella can be reached at 571-272-7778. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/MICHAEL ROBERT CAMMARATA/Primary Examiner, Art Unit 2667
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