DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (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.
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.
Claim(s) 1-20 is/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.
For claim 1, the claim language “determining a sensor operating condition associated with the CGM sensor data” does not appear to be 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 pre-AIA the inventor(s), at the time the application was filed, had possession of the claimed invention. A claim may lack written description when the specification does not disclose the computer and the algorithm (i.e., the necessary steps and/or flowcharts) that perform the claimed function in sufficient detail such that one of ordinary skill in the art can reasonably conclude that the inventor invented the claimed subject matter. See MPEP 2161.01(I). Here, the claim recites the function of determining a sensor operating condition associated with the CGM sensor data, but the specification never discloses the necessary steps and/or flowcharts of how this occurs. It is not enough that a skilled artisan could devise a way to accomplish the function because this is not relevant to the issue of whether the inventor has shown possession of the claimed invention. See MPEP 2161.01(I). Therefore, adequate disclosure is needed.
For claim 1, the claim language “determining an estimated sensor glucose value based on the weights and outputs of the plurality of machine learning models in response to the CGM sensor data” does not appear to be 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 pre-AIA the inventor(s), at the time the application was filed, had possession of the claimed invention. A claim may lack written description when the specification does not disclose the computer and the algorithm (i.e., the necessary steps and/or flowcharts) that perform the claimed function in sufficient detail such that one of ordinary skill in the art can reasonably conclude that the inventor invented the claimed subject matter. See MPEP 2161.01(I). Here, the claim recites the function of determining an estimated sensor glucose value based on the weights and outputs of the plurality of machine learning models, but the specification never discloses the necessary steps and/or flowcharts of how this occurs. The term “machine learning models” is treated as a black box and the specification does not describe the specifics of how to achieve the above-recited function(s) with this algorithm. For example, how many and what types of layers are there? How is the data propagated? What logics are programmed to help the machine learning algorithm make a decision? Is the training supervised or unsupervised? How is the clustering problem solved or addressed? Are other training concepts used such as regression? It is not enough that a skilled artisan could devise a way to accomplish the function because this is not relevant to the issue of whether the inventor has shown possession of the claimed invention. See MPEP 2161.01(I). Therefore, adequate disclosure is needed.
For claim 11, the claim language “determining a sensor operating condition associated with the CGM sensor data” does not appear to be 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 pre-AIA the inventor(s), at the time the application was filed, had possession of the claimed invention. A claim may lack written description when the specification does not disclose the computer and the algorithm (i.e., the necessary steps and/or flowcharts) that perform the claimed function in sufficient detail such that one of ordinary skill in the art can reasonably conclude that the inventor invented the claimed subject matter. See MPEP 2161.01(I). Here, the claim recites the function of determining a sensor operating condition associated with the CGM sensor data, but the specification never discloses the necessary steps and/or flowcharts of how this occurs. It is not enough that a skilled artisan could devise a way to accomplish the function because this is not relevant to the issue of whether the inventor has shown possession of the claimed invention. See MPEP 2161.01(I). Therefore, adequate disclosure is needed.
For claim 11, the claim language “determining an estimated sensor glucose value based on the weights and outputs of the plurality of machine learning models in response to the CGM sensor data” does not appear to be 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 pre-AIA the inventor(s), at the time the application was filed, had possession of the claimed invention. A claim may lack written description when the specification does not disclose the computer and the algorithm (i.e., the necessary steps and/or flowcharts) that perform the claimed function in sufficient detail such that one of ordinary skill in the art can reasonably conclude that the inventor invented the claimed subject matter. See MPEP 2161.01(I). Here, the claim recites the function of determining an estimated sensor glucose value based on the weights and outputs of the plurality of machine learning models, but the specification never discloses the necessary steps and/or flowcharts of how this occurs. The term “machine learning models” is treated as a black box and the specification does not describe the specifics of how to achieve the above-recited function(s) with this algorithm. For example, how many and what types of layers are there? How is the data propagated? What logics are programmed to help the machine learning algorithm make a decision? Is the training supervised or unsupervised? How is the clustering problem solved or addressed? Are other training concepts used such as regression? It is not enough that a skilled artisan could devise a way to accomplish the function because this is not relevant to the issue of whether the inventor has shown possession of the claimed invention. See MPEP 2161.01(I). Therefore, adequate disclosure is needed.
For claim 18, the claim language “determining an estimated sensor glucose value based on weighting outputs of the plurality of machine learning models in response to the sensor data” does not appear to be 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 pre-AIA the inventor(s), at the time the application was filed, had possession of the claimed invention. A claim may lack written description when the specification does not disclose the computer and the algorithm (i.e., the necessary steps and/or flowcharts) that perform the claimed function in sufficient detail such that one of ordinary skill in the art can reasonably conclude that the inventor invented the claimed subject matter. See MPEP 2161.01(I). Here, the claim recites the function of determining an estimated sensor glucose value based on weighting outputs of the plurality of machine learning models, but the specification never discloses the necessary steps and/or flowcharts of how this occurs. The term “machine learning models” is treated as a black box and the specification does not describe the specifics of how to achieve the above-recited function(s) with this algorithm. For example, how many and what types of layers are there? How is the data propagated? What logics are programmed to help the machine learning algorithm make a decision? Is the training supervised or unsupervised? How is the clustering problem solved or addressed? Are other training concepts used such as regression? It is not enough that a skilled artisan could devise a way to accomplish the function because this is not relevant to the issue of whether the inventor has shown possession of the claimed invention. See MPEP 2161.01(I). Therefore, adequate disclosure is needed.
For claim 19, the claim language “determining a sensor operating condition associated with the sensor data” does not appear to be 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 pre-AIA the inventor(s), at the time the application was filed, had possession of the claimed invention. A claim may lack written description when the specification does not disclose the computer and the algorithm (i.e., the necessary steps and/or flowcharts) that perform the claimed function in sufficient detail such that one of ordinary skill in the art can reasonably conclude that the inventor invented the claimed subject matter. See MPEP 2161.01(I). Here, the claim recites the function of determining a sensor operating condition associated with the CGM sensor data, but the specification never discloses the necessary steps and/or flowcharts of how this occurs. It is not enough that a skilled artisan could devise a way to accomplish the function because this is not relevant to the issue of whether the inventor has shown possession of the claimed invention. See MPEP 2161.01(I). Therefore, adequate disclosure is needed.
Dependent claim(s) 2-10, 12-18, and 19-20 fail to cure the deficiencies of independent claim(s) 1, 11, and 18, thus claim(s) 1-20 is/are rejected under 35 U.S.C. 112(a).
Claim(s) 1-20 is/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 enablement requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to enable one skilled in the art to which it pertains, or with which it is most nearly connected, to make and/or use the invention.
For claim 1, the claim language “wherein each machine learning model of the plurality of machine learning models is trained to predict a sensor glucose value under a respective sensor operating condition; determining a sensor operating condition associated with the CGM sensor data; determining an estimated sensor glucose value based on the weights and outputs of the plurality of machine learning models in response to the CGM sensor data” lacks enablement. Going through the In re Wands factors, it is completely unclear which sensor operating conditions are associated with which CGM sensor data and how the plurality of machine learning models are trained to determine/predict a sensor glucose value based on the weights and outputs of the plurality of machine learning models in response to the CGM sensor data. While the written description states, at para [0060] as originally filed, that “pattern, trend, or behavior of one or more input features of the sensor data (e.g., sensitivity loss, sensitivity increases, spikes, drop-offs, periodic behaviors, etc.),” this encompasses the detection of any slope, peak, frequency component, baseline shift of or even periodically recurring events with the analyzed signal, rendering it unclear which characteristics to chose from. It is unclear how these characteristics are selected as representatives for a particular sensor operating condition, and which are to be discarded. Even assuming a skilled artisan would choose arbitrary characteristics for each model, it is unclear how these would differ from each other in terms of their characteristics, further rendering the selection procedure to be applied afterwards unclear. It is further not disclosed how characteristics are chosen which are not common with a plurality of sensor operating conditions or even more generic models created in absence of the sensor operating conditions. Therefore, when looking at the In re Wands factors, the (a) breadth of the claims is generic, (b) the nature of the invention is very broad and limitless, (c) the state of the prior art is thin, (d) the level of skill of the skilled artisan may be somewhat high or may just be a bachelor’s in engineering, (e) the level of predictability is low since each machine learning model is unique, (f) the amount of direction provided is also low and very generic (as discussed above), (g) the existence of working examples is low (as discussed above, there not being a single, concrete example in the written description), and (h) the quantity of experimentation being huge because the skilled artisan would have to analyze every single data set for any characteristic they might contain, determine a set of characteristics being representative for said data set while ensuring that these are mutually exclusive with the other sets of characteristics of the other outlier models as well as general characteristics found in a model in the absence of any sensor operating condition, and derive a metric (e.g., of similarity) for deciding on which model to choose form or which weights to apply to which model. It should be understood that merely illustrating an abstract modality (such as a data point, signal, or image) as an input to a machine learning model for training in not considered a disclosure of a training data set suitable for training the machine learning model in absence of any indications which features are necessarily extracted and fed into the actual model. It is also noted that the databases disclosed in the written description are also devoid of further descriptions as to how those databases would first be constructed, filled with at least the conditions necessary for training the model, without knowing which these could be, and correlated to the input features with the sensor data.
For claim 11, the claim language “wherein each machine learning model of the plurality of machine learning models is trained to predict a sensor glucose value under a respective sensor operating condition; determining a sensor operating condition associated with the CGM sensor data; determining an estimated sensor glucose value based on the weights and outputs of the plurality of machine learning models in response to the CGM sensor data” lacks enablement. Going through the In re Wands factors, it is completely unclear which sensor operating conditions are associated with which CGM sensor data and how the plurality of machine learning models are trained to determine/predict a sensor glucose value based on the weights and outputs of the plurality of machine learning models in response to the CGM sensor data. While the written description states, at para [0060] as originally filed, that “pattern, trend, or behavior of one or more input features of the sensor data (e.g., sensitivity loss, sensitivity increases, spikes, drop-offs, periodic behaviors, etc.),” this encompasses the detection of any slope, peak, frequency component, baseline shift of or even periodically recurring events with the analyzed signal, rendering it unclear which characteristics to chose from. It is unclear how these characteristics are selected as representatives for a particular sensor operating condition, and which are to be discarded. Even assuming a skilled artisan would choose arbitrary characteristics for each model, it is unclear how these would differ from each other in terms of their characteristics, further rendering the selection procedure to be applied afterwards unclear. It is further not disclosed how characteristics are chosen which are not common with a plurality of sensor operating conditions or even more generic models created in absence of the sensor operating conditions. Therefore, when looking at the In re Wands factors, the (a) breadth of the claims is generic, (b) the nature of the invention is very broad and limitless, (c) the state of the prior art is thin, (d) the level of skill of the skilled artisan may be somewhat high or may just be a bachelor’s in engineering, (e) the level of predictability is low since each machine learning model is unique, (f) the amount of direction provided is also low and very generic (as discussed above), (g) the existence of working examples is low (as discussed above, there not being a single, concrete example in the written description), and (h) the quantity of experimentation being huge because the skilled artisan would have to analyze every single data set for any characteristic they might contain, determine a set of characteristics being representative for said data set while ensuring that these are mutually exclusive with the other sets of characteristics of the other outlier models as well as general characteristics found in a model in the absence of any sensor operating condition, and derive a metric (e.g., of similarity) for deciding on which model to choose form or which weights to apply to which model. It should be understood that merely illustrating an abstract modality (such as a data point, signal, or image) as an input to a machine learning model for training in not considered a disclosure of a training data set suitable for training the machine learning model in absence of any indications which features are necessarily extracted and fed into the actual model. It is also noted that the databases disclosed in the written description are also devoid of further descriptions as to how those databases would first be constructed, filled with at least the conditions necessary for training the model, without knowing which these could be, and correlated to the input features with the sensor data.
For claim 18, the claim language “wherein each machine learning model of the plurality of machine learning models is trained to predict a sensor glucose value under a respective sensor operating condition; determining an estimated sensor glucose value based on weighting outputs of the plurality of machine learning models in response to the sensor data” lacks enablement. Going through the In re Wands factors, it is completely unclear which sensor operating conditions are associated with which CGM sensor data and how the plurality of machine learning models are trained to determine/predict a sensor glucose value based on the weights and outputs of the plurality of machine learning models in response to the CGM sensor data. While the written description states, at para [0060] as originally filed, that “pattern, trend, or behavior of one or more input features of the sensor data (e.g., sensitivity loss, sensitivity increases, spikes, drop-offs, periodic behaviors, etc.),” this encompasses the detection of any slope, peak, frequency component, baseline shift of or even periodically recurring events with the analyzed signal, rendering it unclear which characteristics to chose from. It is unclear how these characteristics are selected as representatives for a particular sensor operating condition, and which are to be discarded. Even assuming a skilled artisan would choose arbitrary characteristics for each model, it is unclear how these would differ from each other in terms of their characteristics, further rendering the selection procedure to be applied afterwards unclear. It is further not disclosed how characteristics are chosen which are not common with a plurality of sensor operating conditions or even more generic models created in absence of the sensor operating conditions. Therefore, when looking at the In re Wands factors, the (a) breadth of the claims is generic, (b) the nature of the invention is very broad and limitless, (c) the state of the prior art is thin, (d) the level of skill of the skilled artisan may be somewhat high or may just be a bachelor’s in engineering, (e) the level of predictability is low since each machine learning model is unique, (f) the amount of direction provided is also low and very generic (as discussed above), (g) the existence of working examples is low (as discussed above, there not being a single, concrete example in the written description), and (h) the quantity of experimentation being huge because the skilled artisan would have to analyze every single data set for any characteristic they might contain, determine a set of characteristics being representative for said data set while ensuring that these are mutually exclusive with the other sets of characteristics of the other outlier models as well as general characteristics found in a model in the absence of any sensor operating condition, and derive a metric (e.g., of similarity) for deciding on which model to choose form or which weights to apply to which model. It should be understood that merely illustrating an abstract modality (such as a data point, signal, or image) as an input to a machine learning model for training in not considered a disclosure of a training data set suitable for training the machine learning model in absence of any indications which features are necessarily extracted and fed into the actual model. It is also noted that the databases disclosed in the written description are also devoid of further descriptions as to how those databases would first be constructed, filled with at least the conditions necessary for training the model, without knowing which these could be, and correlated to the input features with the sensor data.
Dependent claim(s) 2-10, 12-18, and 19-20 fail to cure the deficiencies of independent claim(s) 1, 11, and 18, thus claim(s) 1-20 is/are rejected under 35 U.S.C. 112(a).
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.
Claim(s) 1-20 is/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.
For claim 1, the claim language “a sensor operating condition” is ambiguous. The written description never uses the term “sensor operating condition” and it is unclear whether this means (1) a condition of the sensor and how the sensor itself operates (i.e., the rate at which is samples); or (2) the conditions surround the sensor that the sensor has to operate it (i.e., environmental temperature, moisture, etc.). The written description uses the term “outlier condition” to describe a “wide range of conditions that sensor devices face” (see para [0007] of the written description as originally filed) and the claim language will be examined to mean “outlier condition” (see that seems to be what the term means in light of the specification).
For claim 11, the claim language “a sensor operating condition” is ambiguous. The written description never uses the term “sensor operating condition” and it is unclear whether this means (1) a condition of the sensor and how the sensor itself operates (i.e., the rate at which is samples); or (2) the conditions surround the sensor that the sensor has to operate it (i.e., environmental temperature, moisture, etc.). The written description uses the term “outlier condition” to describe a “wide range of conditions that sensor devices face” (see para [0007] of the written description as originally filed) and the claim language will be examined to mean “outlier condition” (see that seems to be what the term means in light of the specification).
For claim 18, the claim language “sensor operating condition” is ambiguous. The written description never uses the term “sensor operating condition” and it is unclear whether this means (1) a condition of the sensor and how the sensor itself operates (i.e., the rate at which is samples); or (2) the conditions surround the sensor that the sensor has to operate it (i.e., environmental temperature, moisture, etc.). The written description uses the term “outlier condition” to describe a “wide range of conditions that sensor devices face” (see para [0007] of the written description as originally filed) and the claim language will be examined to mean “outlier condition” (see that seems to be what the term means in light of the specification).
Dependent claim(s) 2-10, 12-18, and 19-20 fail to cure the ambiguity of independent claim(s) 1, 11, and 18, thus claim(s) 1-20 is/are rejected under 35 U.S.C. 112(b).
Allowable Subject Matter
Claim(s) 1-20 would be allowable if rewritten or amended to overcome the rejection(s) under 35 U.S.C. 112(a) and 35 U.S.C. 112(b), set forth in this Office action.
The following is a statement of reasons for the indication of allowable subject matter:
U.S. Patent Application Publication No. 2020/0375549 to Wexler et al. (hereinafter “Wexler”) discloses a computer-implemented method (Abstract) comprising: receiving continuous glucose monitoring sensor data measured by a sensor device (para [0062]); inputting the CGM sensor data into a plurality of machine learning models (para [0065] and [0071]), wherein each machine learning model of the plurality of machine learning models is trained to predict a sensor glucose value under a respective sensor operating condition (para [0065] and [0071]); determining a sensor operating condition associated with the CGM sensor data (para [0063] and/or [0067]); determining an estimated sensor glucose value (para [0064] and [0070]); and causing displaying of the estimated sensor glucose value on a display interface (para [0074]) (also see Figs. 6C-D and para [0112] and [0117]-[0119]).
U.S. Patent Application Publication No. 2017/0185733 to Nogueira et al. (hereinafter “Nogueira”) discloses determining weights of the plurality of machine learning models based on a sensor operating condition (para [0795]) (also para [0778]) and [0781]-[0794]) and displaying such that sensor glucose signal blanking is reduced (para [0754] and [0796]).
However, the prior art of record does not disclose and would not have rendered obvious to the ordered combination of elements recited in the claim(s).
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/apply/applying-online/eterminal-disclaimer.
Claim(s) 18 is/are rejected on the ground of nonstatutory double patenting as being unpatentable over claim(s) 1 of U.S. Patent No. 12,161,464 (hereinafter “the ‘464 application”). Although the claims at issue are not identical, they are not patentably distinct from each other because the claims of the instant application are broader than the claims of the ‘464 application.
Conclusion
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/DANIEL L CERIONI/Primary Examiner, Art Unit 3791
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