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 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 5 and 6 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.
The term “low” in claim 5 is a relative and subjective term which renders the claim indefinite. The term “low” is not 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. It is not clear what is defined as a “low level”.
Similarly, the term “large” in claim 6 is a relative and subjective term which renders the claim indefinite. The term “large” is not 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. It is not clear what is defined as a “large contribution degree”.
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 1-19 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Specifically, representative Claim 1 recites:
An error factor estimation device that estimates an error factor of an inspection result which becomes erroneous, the error factor estimation device comprising:
a computer system including one processor or a plurality of processors and one memory or a plurality of memories,
wherein the computer system executes a first feature generating process of processing data including the inspection result collected from an inspection device and generating a plurality of feature quantities,
a model generating process of generating a first model for training a relationship between errors and the plurality of feature quantities generated through the first feature generating process,
a contribution degree calculating process of calculating a contribution degree indicating the degree of contribution of at least one of the plurality of feature quantities used for training for the first model to an output of the first model, and
an error factor acquisition process of acquiring error factors labeled with feature quantities or combinations of the feature quantities selected based on the contribution degree calculated through the contribution degree calculating 39 process or usefulness calculated from the contribution degree.
The claim limitations in the abstract idea have been highlighted in bold above; the remaining limitations are “additional elements”.
Under the Step 1 of the eligibility analysis, we determine whether the claims are to a statutory category by considering whether the claimed subject matter falls within the four statutory categories of patentable subject matter identified by 35 U.S.C. 101: Process, machine, manufacture, or composition of matter. The above claim is considered to be in a statutory category (machine).
Under the Step 2A, Prong One, we consider whether the claim recites a judicial exception (abstract idea). In the above claim, the highlighted portion constitutes an abstract idea because, under a broadest reasonable interpretation, it recites limitations that fall into/recite an abstract idea exceptions. Specifically, under the 2019 Revised Patent Subject matter Eligibility Guidance, it falls into the grouping of subject matter when recited as such in a claim limitation, that covers mathematical concepts (mathematical relationships, mathematical formulas or equations, mathematical calculations) and mental processes – concepts performed in the human mind including an observation, evaluation, judgement, and/or opinion.
For example, steps of “a model generating process of generating a first model for training a relationship between errors and the plurality of feature quantities generated through the first feature generating process (defining a relationship between variables),
a contribution degree calculating process of calculating a contribution degree indicating the degree of contribution of at least one of the plurality of feature quantities used for training for the first model to an output of the first model (calculation), and
an error factor acquisition process of acquiring error factors labeled with feature quantities or combinations of the feature quantities selected based on the contribution degree calculated through the contribution degree calculating 39 process or usefulness calculated from the contribution degree (calculation)” are treated by the Examiner as belonging to mathematical concept grouping, while the steps of “wherein the computer system executes a first feature generating process of processing data including the inspection result collected from an inspection device and generating a plurality of feature quantities (generating and organizing data)” are treated as belonging to mental process grouping.
Similar limitations comprise the abstract ideas of Claims 14 and 17.
Next, under the Step 2A, Prong Two, we consider whether the claim that recites a judicial exception is integrated into a practical application.
In this step, we evaluate whether the claim recites additional elements that integrate the exception into a practical application of that exception.
The above claims comprise the following additional elements:
Claim 1: An error factor estimation device that estimates an error factor of an inspection result which becomes erroneous, the error factor estimation device comprising: a computer system including one processor or a plurality of processors and one memory or a plurality of memories
Claim 14: An error factor estimation method of estimating an error factor of an inspection result which becomes erroneous;
Claim 17: A non-transitory computer-readable medium storing a program command for executing an error factor estimation method of estimating an error factor of an inspection result which becomes erroneous.
The additional element in the preamble of “An error factor estimation device/method that estimates an error factor of an inspection result which becomes erroneous” is not qualified for a meaningful limitation because it only generally links the use of the judicial exception to a particular technological environment or field of use. A non-transitory computer-readable medium and one memory or a plurality of memories (generic memories) and a computer system including one processor or a plurality of processors (generic processors) are generally recited and are not qualified as particular machines.
In conclusion, the above additional elements, considered individually and in combination with the other claim elements do not reflect an improvement to other technology or technical field, and, therefore, do not integrate the judicial exception into a practical application. Therefore, the claims are directed to a judicial exception and require further analysis under the Step 2B.
However, the above claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception (Step 2B analysis).
The claims, therefore, are not patent eligible.
With regards to the dependent claims, claims 2-13, 15, 16, and 18-20 provide additional features/steps which are part of an expanded algorithm, so these limitations should be considered part of an expanded abstract idea of the independent claims.
Claim Rejections - 35 USC § 102
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 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) 1-3, 8-12, and 14-19 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Hama et al. (US 20200242489 A1).
Regarding Claim 1, Hama teaches an error factor estimation device that estimates an error factor of an inspection result which becomes erroneous, the error factor estimation device comprising:
a computer system including one processor or a plurality of processors and one memory or a plurality of memories (Hama [0057] The computer system includes a plurality of computers 100-1, 100-2, and 100-3, and a terminal 101. The plurality of computers 100-1, 100-2, and 100-3 and the terminal 101 are connected to each other via a network 102.),
wherein the computer system executes a first feature generating process of processing data including the inspection result collected from an inspection device and generating a plurality of feature quantities (Hama [0063] The computer 100-2 calculates, for a value of each feature quantity contained in the input data, a contribution value (a first evaluation value) which represents magnitude of contribution (magnitude of influence) to a predicted value of a value of a feature quantity.),
a model generating process of generating a first model for training a relationship between errors and the plurality of feature quantities generated through the first feature generating process (Hama [0062] The predictor 110 performs processing for the input data based on model information defining the model, and outputs a predicted value.),
a contribution degree calculating process of calculating a contribution degree indicating the degree of contribution of at least one of the plurality of feature quantities used for training for the first model to an output of the first model (Hama [0063] The computer 100-2 includes a contribution value calculation unit 120. Also see [0066] The contribution value calculation unit 120 calculates, as the contribution value, a value indicating how much the evaluation target data 300 contributes to the predicted value by having such a feature quantity as compared with the comparison target data 400.), and
an error factor acquisition process of acquiring error factors labeled with feature quantities or combinations of the feature quantities selected based on the contribution degree calculated through the contribution degree calculating process or usefulness calculated from the contribution degree (Hama [0061] The computer 100-1 executes processing for the input data based on a model (algorithm) for predicting an event of a target, and outputs a predicted value (prediction result). The output predicted value is, for example, a classification result of the input data and an occurrence probability of any risk. The computer 100-1 includes a predictor 110.).
Regarding Claim 2, Hama further teaches an error factor list in which the feature quantities labeled with the error factors are stored (Hama [0069] The interpretation factor conversion information 132 is information for managing an interpretation factor which is determined by a value and a contribution value of a feature quantity contained in the evaluation target data 300. Also see [0092] The interpretation factor conversion information 132 is information in which a set including, as elements, a combination of a value and contribution value of the feature quantity contained in the input data is associated with an interpretation factor. The interpretation factor conversion information 132 according to the first embodiment includes a conversion table 500 associated with identification information of the respective feature quantities.), and
acquires an error factor labeled with the feature quantities selected based on the contribution degree or the usefulness with reference to the error factor list in the error factor acquisition process (Hama [0070] The interpretation factor selection unit 130 specifies a corresponding interpretation factor by referring to the interpretation factor conversion information 132 based on the value and the contribution value of the feature quantity, and calculates a score of the interpretation factor.).
Regarding Claim 3, Hama further teaches a dictionary in which the error factors are labeled with the combinations of the feature quantities (Hama [0069] The interpretation factor conversion information 132 is information for managing an interpretation factor which is determined by a value and a contribution value of a feature quantity contained in the evaluation target data 300. Also see [0092] The interpretation factor conversion information 132 is information in which a set including, as elements, a combination of a value and contribution value of the feature quantity contained in the input data is associated with an interpretation factor. The interpretation factor conversion information 132 according to the first embodiment includes a conversion table 500 associated with identification information of the respective feature quantities.), and
acquires an error factor labeled with a combination identical or similar to a combination of the feature quantities selected based on the contribution degree or the usefulness with reference to the dictionary in the error factor acquisition process (Hama [0070] The interpretation factor selection unit 130 specifies a corresponding interpretation factor by referring to the interpretation factor conversion information 132 based on the value and the contribution value of the feature quantity, and calculates a score of the interpretation factor.).
Regarding Claim 8, Hama further teaches a selection process of selecting the plurality of feature quantities generated through the first feature generating process from the plurality of feature quantities (Hama [0110] The feature quantity selection field 907 is a field for selecting a feature quantity.).
Regarding Claim 9, Hama further teaches a display control process of causing a display unit to display (Hama [0155] FIG. 13 shows an example of a result output screen 1300 displayed on the terminal 101 according to the first embodiment.) a list of the feature quantities or a trend of the feature quantities selected based on the contribution degree, the usefulness, or the error factors acquired through the error factor acquisition process (Hama [0157] The predicted value display field 1301 is a field for displaying a predicted value of the evaluation target data 300. The interpretation factor display field 1302 is a field for displaying an interpretation factor. In the interpretation factor display field 1302, the score 802 corresponding to the interpretation factor 801 of each entry of the interpretation information 610 is displayed as a bar graph.).
Regarding Claim 10, Hama further teaches a model for training a classification method of classifying error records and normal records using the plurality of feature quantities generated through the first feature generating process is generated (Hama [0061] The computer 100-1 executes processing for the input data based on a model (algorithm) for predicting an event of a target, and outputs a predicted value (prediction result). The output predicted value is, for example, a classification result of the input data and an occurrence probability of any risk. The computer 100-1 includes a predictor 110.).
Regarding Claim 11, Hama further teaches a model for training an error probability of each record estimated based on a positional relationship between error records and normal records in a feature space of the plurality of feature quantities is generated (Hama [0061] The computer 100-1 executes processing for the input data based on a model (algorithm) for predicting an event of a target, and outputs a predicted value (prediction result). The output predicted value is, for example, a classification result of the input data and an occurrence probability of any risk. The computer 100-1 includes a predictor 110.).
Regarding Claim 12, Hama further teaches wherein the feature quantity is an index related to a variation in an inspection result (Hama [0210] The radio button 2142 is a radio button operated to equally divide the value range of the value into two parts based on statistics such as a median value, an average value, and a mode value. See Fig. 21 2142).
Regarding Claim 14, Hama teaches an error factor estimation method of estimating an error factor of an inspection result which becomes erroneous, the method comprising:
processing data including the inspection result collected from an inspection device and generating a plurality of feature quantities (Hama [0063] The computer 100-2 calculates, for a value of each feature quantity contained in the input data, a contribution value (a first evaluation value) which represents magnitude of contribution (magnitude of influence) to a predicted value of a value of a feature quantity.),
generating a first model for training a relationship between errors and the plurality of generated feature quantities (Hama [0062] The predictor 110 performs processing for the input data based on model information defining the model, and outputs a predicted value.);
calculating a contribution degree indicating the degree of contribution of at least one of the plurality of feature quantities used for training for the first model to an output of the first model (Hama [0063] The computer 100-2 includes a contribution value calculation unit 120. Also see [0066] The contribution value calculation unit 120 calculates, as the contribution value, a value indicating how much the evaluation target data 300 contributes to the predicted value by having such a feature quantity as compared with the comparison target data 400.), and
acquiring error factors labeled with feature quantities or combinations of the feature quantities selected based on the calculated contribution degree or usefulness calculated from the contribution degree (Hama [0061] The computer 100-1 executes processing for the input data based on a model (algorithm) for predicting an event of a target, and outputs a predicted value (prediction result). The output predicted value is, for example, a classification result of the input data and an occurrence probability of any risk. The computer 100-1 includes a predictor 110.).
Regarding Claim 15, Hama further teaches supplying an error factor list in which the feature quantities labeled with the error factors are stored (Hama [0069] The interpretation factor conversion information 132 is information for managing an interpretation factor which is determined by a value and a contribution value of a feature quantity contained in the evaluation target data 300. Also see [0092] The interpretation factor conversion information 132 is information in which a set including, as elements, a combination of a value and contribution value of the feature quantity contained in the input data is associated with an interpretation factor. The interpretation factor conversion information 132 according to the first embodiment includes a conversion table 500 associated with identification information of the respective feature quantities.), and
wherein the acquiring of the error factors includes acquiring an error factor labeled with the feature quantities selected based on the contribution degree or the usefulness with reference to the error factor list (Hama [0070] The interpretation factor selection unit 130 specifies a corresponding interpretation factor by referring to the interpretation factor conversion information 132 based on the value and the contribution value of the feature quantity, and calculates a score of the interpretation factor.).
Regarding Claim 16, Hama further teaches supplying a dictionary in which the error factors are 45 labeled with the combinations of the feature quantities (Hama [0069] The interpretation factor conversion information 132 is information for managing an interpretation factor which is determined by a value and a contribution value of a feature quantity contained in the evaluation target data 300. Also see [0092] The interpretation factor conversion information 132 is information in which a set including, as elements, a combination of a value and contribution value of the feature quantity contained in the input data is associated with an interpretation factor. The interpretation factor conversion information 132 according to the first embodiment includes a conversion table 500 associated with identification information of the respective feature quantities.), and
wherein the acquiring of the error factors includes acquiring an error factor labeled with a combination identical or similar to a combination of the feature quantities selected based on the contribution degree or the usefulness with reference to the dictionary (Hama [0070] The interpretation factor selection unit 130 specifies a corresponding interpretation factor by referring to the interpretation factor conversion information 132 based on the value and the contribution value of the feature quantity, and calculates a score of the interpretation factor.).
Regarding Claim 17, Hama teaches a non-transitory computer-readable medium storing a program command for executing an error factor estimation method of estimating an error factor of an inspection result which becomes erroneous (Hama [0394] Each of the configurations, functions, processing units, processing methods described above may be partially or entirely implemented by hardware such as by designing with an integrated circuit. Further, the invention can also be implemented by program code of software that implements the functions of the embodiments. In this case, a storage medium storing the program code is provided to a computer, and a processor provided in the computer reads out the program code stored in the storage medium. In this case, the program code itself read out from the storage medium implements the functions of the embodiments described above, and the program code itself and the storage medium storing the program code constitute the invention.), wherein the error factor estimation method includes
processing data including the inspection result collected from an inspection device and generating a plurality of feature quantities (Hama [0063] The computer 100-2 calculates, for a value of each feature quantity contained in the input data, a contribution value (a first evaluation value) which represents magnitude of contribution (magnitude of influence) to a predicted value of a value of a feature quantity.),
generating a first model for training a relationship between errors and the plurality of generated feature quantities (Hama [0062] The predictor 110 performs processing for the input data based on model information defining the model, and outputs a predicted value.);
calculating a contribution degree indicating the degree of contribution of at least one of the plurality of feature quantities used for training for the first model to an output of the first model (Hama [0063] The computer 100-2 includes a contribution value calculation unit 120. Also see [0066] The contribution value calculation unit 120 calculates, as the contribution value, a value indicating how much the evaluation target data 300 contributes to the predicted value by having such a feature quantity as compared with the comparison target data 400.), and
acquiring error factors labeled with feature quantities or combinations of the feature quantities selected based on the calculated contribution degree or usefulness calculated from the contribution degree (Hama [0061] The computer 100-1 executes processing for the input data based on a model (algorithm) for predicting an event of a target, and outputs a predicted value (prediction result). The output predicted value is, for example, a classification result of the input data and an occurrence probability of any risk. The computer 100-1 includes a predictor 110.).
Regarding Claim 18, Hama further teaches supplying an error factor list in which the feature quantities are labeled with the error factors (Hama [0069] The interpretation factor conversion information 132 is information for managing an interpretation factor which is determined by a value and a contribution value of a feature quantity contained in the evaluation target data 300. Also see [0092] The interpretation factor conversion information 132 is information in which a set including, as elements, a combination of a value and contribution value of the feature quantity contained in the input data is associated with an interpretation factor. The interpretation factor conversion information 132 according to the first embodiment includes a conversion table 500 associated with identification information of the respective feature quantities.), and
wherein the acquiring of the error factors includes acquiring an error factor labeled with the feature quantities selected based on the contribution degree or the usefulness with reference to the error factor list (Hama [0070] The interpretation factor selection unit 130 specifies a corresponding interpretation factor by referring to the interpretation factor conversion information 132 based on the value and the contribution value of the feature quantity, and calculates a score of the interpretation factor.).
Regarding Claim 19, Hama further teaches supplying a dictionary in which the error factors are labeled with the combinations of the feature quantities (Hama [0069] The interpretation factor conversion information 132 is information for managing an interpretation factor which is determined by a value and a contribution value of a feature quantity contained in the evaluation target data 300. Also see [0092] The interpretation factor conversion information 132 is information in which a set including, as elements, a combination of a value and contribution value of the feature quantity contained in the input data is associated with an interpretation factor. The interpretation factor conversion information 132 according to the first embodiment includes a conversion table 500 associated with identification information of the respective feature quantities.), and
wherein the acquiring of the error factors includes acquiring an error factor labeled with a combination identical or similar to a combination of the feature quantities selected based on the contribution degree or the usefulness with reference to the dictionary (Hama [0070] The interpretation factor selection unit 130 specifies a corresponding interpretation factor by referring to the interpretation factor conversion information 132 based on the value and the contribution value of the feature quantity, and calculates a score of the interpretation factor.).
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claim(s) 4 and 6 is/are rejected under 35 U.S.C. 103 as being unpatentable over Hama (as stated above).
Regarding Claim 4, Hama (as stated above) further teaches a weight list in which the plurality of feature quantities are stored in association with weights set in the plurality of feature quantities (Hama [0094] The interpretation factor data 510 includes an interpretation factor 511 and a weight 512. Also see [0096] The weight 512 is a value representing the degree of validity corresponding to the interpretation factor. As will be described below, a score of the interpretation factor is calculated using the weight 512.).
Hama (as stated above) does not explicitly teach executes a usefulness calculating process of calculating the usefulness based on the contribution degree of the feature quantities and the weights stored in association with the feature quantities.
However, Hama teaches that both contribution value and weight are considered factors of the overall analysis (see Hama Fig. 19. Also see [0177] The candidate interpretation factor information 1530 stores entries each including an ID 1901, a feature quantity 1902, an area (value) 1903, an area (contribution value) 1904, a weight 1905, the number of pieces of first data 1906, the number of pieces of second data 1907, an interaction value 1908, and an interpretation factor 1909.).
It would have been obvious to one of ordinary skill in the art, prior to the effective filing date of the instant application, to modify Hama (as stated above) to explicitly teach a usefulness calculating process of calculating the usefulness based on the contribution degree of the feature quantities and the weights stored in association with the feature quantities, because although Hama does not explicitly teach a usefulness metric, Hama incorporates both factors of contribution degree and weight into the disclosed error analysis (Hama [0300] (Processing D2) The candidate interpretation factor generation unit 1401 generates the same number of rows as the number of elements of the target subset in the field of the feature quantity 1902, the field of the area (value) 1903, the field of the area (contribution value) 1904, and the field of the weight 1905 of the added entry. The candidate interpretation factor generation unit 1401 sets the identification information of the feature quantity, which is an element of the target subset, in the field of the feature quantity 1902 of the added entry.).
Regarding Claim 6, Hama (as stated above) further teaches an extraction process of extracting one feature quantity or a plurality of feature quantities with the large contribution degree among the plurality of feature quantities (Hama [0300] (Processing D2) The candidate interpretation factor generation unit 1401 generates the same number of rows as the number of elements of the target subset in the field of the feature quantity 1902, the field of the area (value) 1903, the field of the area (contribution value) 1904, and the field of the weight 1905 of the added entry. The candidate interpretation factor generation unit 1401 sets the identification information of the feature quantity, which is an element of the target subset, in the field of the feature quantity 1902 of the added entry.), and
calculates usefulness of the one feature quantity or the plurality of feature quantities extracted through the extraction process in the usefulness calculating process (Hama [0300] (Processing D2) The candidate interpretation factor generation unit 1401 generates the same number of rows as the number of elements of the target subset in the field of the feature quantity 1902, the field of the area (value) 1903, the field of the area (contribution value) 1904, and the field of the weight 1905 of the added entry. The candidate interpretation factor generation unit 1401 sets the identification information of the feature quantity, which is an element of the target subset, in the field of the feature quantity 1902 of the added entry.).
The Examiner notes that there are currently no prior art rejections for claims 5, 7, and 13.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Yoshida et al. (US 20230122653 A1) discloses an Error Cause Estimation Device And Estimation Method, and was reviewed for possible double patenting.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to CHRISTIAN T BRYANT whose telephone number is (571)272-4194. The examiner can normally be reached Monday-Thursday and Alternate Fridays 7:00-4:30.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, CATHERINE RASTOVSKI can be reached at (571) 270-0349. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/CHRISTIAN T BRYANT/Primary Examiner, Art Unit 2857
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