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 .
Priority
Receipt is acknowledged of certified copies of papers required by 37 CFR 1.55.
Information Disclosure Statement
The information disclosure statements (IDS) submitted on 10/13/2022 and 04/19/2023 are in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
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:
Claim 1:
“a storage unit which stores an analysis target data group which has values of respective variables in a variable group and a value of an objective variable per analysis target and an element group that the variable group and each of one or more modulation method(s) for modulating the variable(s) are set as elements”
“a modulation unit which when the element which is selected from the element group is acquired, plans a modulation function of modulating the value of the variable which is contained in the acquired element on the basis of an action history which is the history of the acquired element and modulates the value of the variable per the analysis target on the basis of the modulation function”
“a generation unit which generates image data which gives a point of coordinates which are values of the modulation result and the objective variable to a coordinate space which is defined by a first axis which corresponds to a result of modulation by the modulation unit and a second axis which corresponds to the objective variable per the analysis target”
Claim 2:
“a control unit which generates a control signal for selecting the element from the element group on the basis of the action history and controls the modulation unit on the basis of the control signal”
Claim 3:
“wherein when the image data is generated by the generation unit, the control unit selects the element from the element group and newly generates the control signal”
Claim 4:
“wherein the control unit defines the image data as a first state and defines the element group as a first action, inputs the image data into a first action value function which outputs a value of the first action in the first state on the basis of a first learning parameter, selects a specific element which corresponds to a specific value in the element-based values which are output from the first action value function from the element group, newly generates the control signal which contains the selected specific element and thereby controls the modulation unit”
Claim 6:
“an accumulation unit which accumulates the image data by the generation unit”
“an evaluation unit which evaluates the modulation function on the basis of the modulation result and the value of the objective variable”
“wherein the control unit selects a combination of time-series first image data and second image data which are accumulated from the accumulation unit, defines the image data as a second state and defines the element group as a second action, inputs the second image data into a second action value function for outputting a value of the second action in the second state on the basis of a second learning parameter, adds a result of evaluation of the modulation function by the evaluation unit to a second output result from the second action value function as a reward and thereby calculates a value of the first action as a teacher signal, updates the second learning parameter on the basis of the teacher signal and a first output result which is output in a case where the first image data is input into the second action value function and updates the first learning parameter by the updated second learning parameter”
Claim 7:
“an evaluation unit which evaluates the modulation function on the basis of the values of the modulation result and the objective variable”
“an output unit which outputs the image data to be displayable in a case where a result of evaluation of the modulation function by the evaluation unit is more than a target value”
Claim 8:
“the evaluation unit generates a regression model for regressing the first-axis coordinate value which corresponds to the modulation result with the objective variable and outputs the accuracy of the regression model as a result of evaluation of the modulation function”
Claim 9:
“the evaluation unit generates an identification model which identifies the first-axis coordinate value which corresponds to the modulation result with the objective variable and outputs the accuracy of the identification model as a result of evaluation of the modulation function”
Upon a review of the specification, descriptions of the above limitations are found in Fig. 2, Fig. 5, Fig. 9, Fig. 10, and specification paragraphs [0010], [0034], [0048]-[0050], [0072]-[0074], [0100], and [0111]-[0117].
[0010] A data processing apparatus which becomes one profile of the invention which is disclosed in the present application has a storage unit which stores an analysis target data group which has values of respective variables in a variable group and a value of an objective variable per analysis target and an element group that the variable group and each of one or more modulation method(s) for modulating the variable(s) are set as elements, a modulation unit which when the element which is selected from the element group is acquired, plans a modulation function of modulating the value of the variable which is contained in the acquired element on the basis of an action history which is the history of the acquired element and modulates the value of the variable per the analysis target on the basis of the modulation function and a generation unit which generates image data which gives a point of coordinates which are values of the modulation result and the objective variable to a coordinate space which is defined by a first axis which corresponds to a result of modulation by the modulation unit and a second axis which corresponds to the objective variable per the analysis target.
[0034] The modulation unit 510 configures part of the numerical formula planning AI101 which is illustrated in Fig. 1. The modulation unit 510 sets the variable or the modulation method in the numerical formula 111. The modulation unit 510 has a data load modulator 511 and a data save modulator 512.
[0048] The image generator 520 configures part of the numerical formula planning AI101 which is illustrated in Fig. 1. The image generator 520 acquires the signal x' which is output from the modulation unit 510 and a value in Objective Variable 302 in the analysis target data which is saved in the data memory 500. The signal x' is a set of coordinate values (a one-dimensional vector) which are calculated from the numerical formula 111 per case. The image generator 520plots the coordinate value that the value of the signal x' is set as the X-axis value and the value in Objective Variable 302 is set as the Y-axis value on the coordinate space 110 and thereby draws them as pixels which configure the image data I in the coordinate scape110 that patient data is plotted.
[0049] The evaluator 530 has the regressor 102 which is illustrated in Fig. 1. The evaluator 530 acquires the signal x' which is output from the modulation unit 510 and the value in Objective Variable 302 from the data memory 500. The evaluator 530 calculates a statistical quantity r(t) in the time step t in accordance with the value in Objective Variable 302.
[0050] Specifically, for example, the evaluator 530 executes the regressor 102 and thereby calculates the statistical quantity r(t) which indicates the prediction accuracy for predicting the value in Objective Variable 302 of the patient. The statistical quantity r(t) corresponds to the reward of the reinforcement learning and become a result of evaluation of a way of selecting the numerical formula 111. For example, a determination coefficient R2 and a mean square error MSE can be adopted as the statistical quantity r(t). That is, the evaluator 530 evaluates the way of selecting the signal x' which is the result of modulation on the basis of the numerical formula 111 which is obtained because that signal x' is selected.
[0072] <Image Data Generation Processing> Fig. 9 is a flowchart illustrating a detailed process procedure example of the image data generation processing by the modulation unit 510 and the image generator 520. First, the data load modulator 511 in the modulation unit 510 executes a process (step 5901). Specifically, for example, the data load modulator 511 selects one factor xl from within the ones in Variable Group 303 which is stored in the data memory 500 by a control signal a(t+1) from the controller 540.
[0073] The data save modulator 512 calculates the numerical formula 111 in accordance with the action history data 600 which is updated by the control signal a(t+1), outputs the signal x', saves it in the data memory 500 and outputs it to the image generator 520 (step 5903).
[0074] The image generator 520 plots the patient data on the coordinate space 110on the basis of the signal x' which is output from the modulation unit 510 and the value in Objective Variable 302 which is stored in the data memory 500 and generates the image data I (t+1)(step 3904).
[0100] [S1012] Next, the data processing apparatus 100 displays a result of analysis (step S1012). Specifically, for example, the data processing apparatus 100 loads the data pack D(k) which is saved in the storage device 202, makes the modulation unit 510 execute numerical formula planning by using Action History 601 in the data pack D(k) and displays the planned numerical formula 111 in the numerical formula display region 880.
[0111] (1) For example, the data processing apparatus 100 has the storage unit, the modulation unit 510 and the image generator 520. The analysis target DB 104 which is one example of the storage unit stores the analysis target data group (the analysis target DB 104) which has Variable Group 303 and Objective Variable 302, 1202 per analysis target and the pattern table 208 stores Variable Group 303 and the element group 105 that each of one or more modulation method(s) of modulating the variable is set as Element 402 which indicates the action of the controller 540.
[0112] When Element 402 which is selected from the element group 105 is acquired, the modulation unit 510 plans the numerical formula 111 as the modulation function of modulating the value of the variable in the acquired Element 402 on the basis of Action History 601 which is the history of the acquired Element 402 and modulates the value of the analysis-target-based variable on the basis of the numerical formula 111.
[0113] The image generator 520 generates the image data I that the coordinate point (the patient data) is given to the coordinate space 110 which is defined by the X-axis and the Y-axis per analysis target, with the modulation result (the signal x') from the modulation unit 510 and the value in Objective Variable 302 being set as the coordinate values of the X-axis and the Y-axis respectively.
[0114] (2) In addition, in the above (1), the data processing apparatus 100 has the controller 540. The controller 540 generates the control signal a(t) which selects Element 402 from the element group 105 in the pattern table 208 on the basis of the action history 601 and controls the modulation unit 510 on the basis of the control signal a(t).
[0115] Thereby, the controller 540 can plan the numerical formula 111 on the basis of Action History 601 and can output the coordinate value (the patient data). The image generator 520 can plot the coordinate value (the patient data) on the coordinate space 110 and thereby can generate the image data I(t).
[0116] (3) In addition, in the above (2), when the image data I(t) is generated by the image generator 520, the controller 540 may select Element 402 from the element group 105 and may freshly generate the control signal a(t).
[0117] Thereby, the image generator 520 can generate the image data I(t+1) that the action by the control signal a(t) is reflected and the controller 540 can take the next action in a state which is called the image data I(t+1) like this.
[0125] (7) In addition, in the above (1), the data processing apparatus 100 has the evaluator 530 and the output unit (the output device 204 or the communication IF 205). The evaluator 530 evaluates the numerical formula 111 on the basis of the modulation result (the value that a numerical value in the variable group of the patient data is substituted into the planned numerical formula 111) (the signal x') and the value in Objective Variable 302. In a case where the statistical quantity r(j) which is the evaluation result of the numerical formula 111 by the evaluator 530 is, for example, more than the target value which is input into the target value input region 862, the output unit may output the image data I(j) to be displayable.
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 the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 1-9 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.
Claim limitations in claims 1-4 and 6-9 (as noted above) invoke 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. However, the written description fails to disclose the corresponding structure, material, or acts for performing the entire claimed function and to clearly link the structure, material, or acts to the function. The descriptions in the specification with respect to the “units” (see Fig. 2, Fig. 5, Fig. 9, Fig. 10, and specification paragraphs [0010], [0034], [0048]-[0050], [0072]-[0074], [0100], and [0111]-[0117]) describe the “units” by what they do rather than what they are structurally, and therefore is insufficient in disclosing the corresponding structure, material, or acts for performing the entire claimed function and to clearly link the structure, material, or acts to the function. Therefore, claims 1-9 are rejected under 35 U.S.C. 112(a) or pre-AIA 35 U.S.C. 112, first paragraph, for lack of written description. See MPEP 2181 (IV) (“Merely restating a function associated with a means-plus-function limitation is insufficient to provide the corresponding structure for definiteness. See, e.g., Noah, 675 F.3d at 1317, 102 USPQ2d at 1419; Blackboard, 574 F.3d at 1384; Aristocrat, 521 F.3d at 1334, 86 USPQ2d at 1239. It follows therefore that such a mere restatement of function in the specification without more description of the means that accomplish the function would also likely fail to provide adequate written description under section 112(a) or pre-AIA section 112, first paragraph.”).
Dependent claims 2-9 are rejected based on being directly or indirectly dependent on rejected claim 1.
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-11 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 limitations in claims 1-4 and 6-9 (as noted above) invoke 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. However, the written description fails to disclose the corresponding structure, material, or acts for performing the entire claimed function and to clearly link the structure, material, or acts to the function. The descriptions in the specification with respect to the “units” (see Fig. 2, Fig. 5, Fig. 9, Fig. 10, and specification paragraphs [0010], [0034], [0048]-[0050], [0072]-[0074], [0100], and [0111]-[0117]) describe the “units” by what they do rather than what they are structurally, and therefore is insufficient in disclosing the corresponding structure, material, or acts for performing the entire claimed function and to clearly link the structure, material, or acts to the function. Therefore, the claims are indefinite and are rejected under 35 U.S.C. 112(b) or pre-AIA 35 U.S.C. 112, second paragraph. For examination purposes, the “units” in claims 1-9 have been interpreted as any generic computer or algorithmic component or circuit that performs each of the corresponding functions.
Applicant may:
(a) Amend the claim so that the claim limitation will no longer be interpreted as a limitation under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph;
(b) Amend the written description of the specification such that it expressly recites what structure, material, or acts perform the entire claimed function, without introducing any new matter (35 U.S.C. 132(a)); or
(c) Amend the written description of the specification such that it clearly links the structure, material, or acts disclosed therein to the function recited in the claim, without introducing any new matter (35 U.S.C. 132(a)).
If applicant is of the opinion that the written description of the specification already implicitly or inherently discloses the corresponding structure, material, or acts and clearly links them to the function so that one of ordinary skill in the art would recognize what structure, material, or acts perform the claimed function, applicant should clarify the record by either:
(a) Amending the written description of the specification such that it expressly recites the corresponding structure, material, or acts for performing the claimed function and clearly links or associates the structure, material, or acts to the claimed function, without introducing any new matter (35 U.S.C. 132(a)); or
(b) Stating on the record what the corresponding structure, material, or acts, which are implicitly or inherently set forth in the written description of the specification, perform the claimed function. For more information, see 37 CFR 1.75(d) and MPEP §§ 608.01(o) and 2181.
Claim 1 recites the limitation “the element” in line 7. There is insufficient antecedent basis for this limitation in the claim. For examination purposes, “the element” has been interpreted as “an element”.
Claim 1 recites the limitation “the value of the variable” in line 9. There is insufficient antecedent basis for this limitation in the claim. For examination purposes, “the value of the variable” has been interpreted as “a value of a variable”.
Claim 1 recites the limitation “the history” in lines 10-11. There is insufficient antecedent basis for this limitation in the claim. For examination purposes, “the history” has been interpreted as “a history”.
Claim 1 recites the limitation “the modulation result” in line 15. There is insufficient antecedent basis for this limitation in the claim. For examination purposes, “the modulation result” has been interpreted as “a modulation result”.
Claim 4 recites the limitation “the element-based values” in line 7. There is insufficient antecedent basis for this limitation in the claim. For examination purposes, “the element-based values” has been interpreted as “element-based values”.
Claim 8 recites the limitation “the first-axis coordinate value” in line 4. There is insufficient antecedent basis for this limitation in the claim. For examination purposes, “the first-axis coordinate value” has been interpreted as “a first-axis coordinate value”.
Claim 8 recites the limitation “the accuracy” in line 5. There is insufficient antecedent basis for this limitation in the claim. For examination purposes, “the accuracy” has been interpreted as “an accuracy”.
Claim 9 recites the limitation “the first-axis coordinate value” in line 5. There is insufficient antecedent basis for this limitation in the claim. For examination purposes, “the first-axis coordinate value” has been interpreted as “a first-axis coordinate value”.
Claim 9 recites the limitation “the accuracy” in lines 6-7. There is insufficient antecedent basis for this limitation in the claim. For examination purposes, “the accuracy” has been interpreted as “an accuracy”.
Claim 10 recites the limitation “the element” in line 11. There is insufficient antecedent basis for this limitation in the claim. For examination purposes, “the element” has been interpreted as “an element”.
Claim 10 recites the limitation “the variable” in line 13. There is insufficient antecedent basis for this limitation in the claim. For examination purposes, “the variable” has been interpreted as “a variable”.
Claim 10 recites the limitation “the history” in lines 14-15. There is insufficient antecedent basis for this limitation in the claim. For examination purposes, “the history” has been interpreted as “a history”.
Claim 10 recites the limitation “the modulation result” in line 19. There is insufficient antecedent basis for this limitation in the claim. For examination purposes, “the modulation result” has been interpreted as “a modulation result”.
Claim 11 recites the limitation “the element” in line 8. There is insufficient antecedent basis for this limitation in the claim. For examination purposes, “the element” has been interpreted as “an element”.
Claim 11 recites the limitation “the variable” in line 10. There is insufficient antecedent basis for this limitation in the claim. For examination purposes, “the variable” has been interpreted as “a variable”.
Claim 11 recites the limitation “the history” in lines 11-12. There is insufficient antecedent basis for this limitation in the claim. For examination purposes, “the history” has been interpreted as “a history”.
Claim 11 recites the limitation “the modulation result” in line 16. There is insufficient antecedent basis for this limitation in the claim. For examination purposes, “the modulation result” has been interpreted as “a modulation result”.
Dependent claims 2-9 are rejected based on being directly or indirectly dependent on rejected claim 1.
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.
Claim 11 is rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. The claim does not fall within at least one of the four categories of patent eligible subject matter because, under their broadest reasonable interpretation in light of the specification, the claim is directed to software per se.
Regarding claim 11, the claim is directed to a “data processing program”. It is not directed to the medium on which the program is stored, nor does the specification provide any indication that the “data processing program” is anything other than software.
Claims 1-11 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Regarding Claim 1,
Claim 1 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 1 is directed to an apparatus, which is directed to a machine, one of the statutory categories.
Step 2A Prong One Analysis: The limitations:
“when the element which is selected from the element group is acquired, plans a modulation function of modulating the value of the variable which is contained in the acquired element on the basis of an action history which is the history of the acquired element”
“modulates the value of the variable per the analysis target on the basis of the modulation function”
“generates image data which gives a point of coordinates which are values of the modulation result and the objective variable to a coordinate space which is defined by a first axis which corresponds to a result of modulation by the modulation unit and a second axis which corresponds to the objective variable per the analysis target”
As drafted, under their broadest reasonable interpretations, cover mental processes (concepts performed in the human mind (including an observation, evaluation, judgement, opinion)) and mathematical concepts (mathematical relationships, mathematical formulas or equations, mathematical calculations) but for the recitation of mere instructions to apply language (See MPEP 2106.05(f)) and insignificant extra-solution activity (See MPEP 2106.05(g)). The above limitations in the context of this claim encompass when a selected element is acquired, planning a modulation function for modulating the value of the variable in the acquired element based on an action history of the acquired element (corresponds to evaluation and judgement; in particular, a human, with the assistance of pen and paper, can plan a modulation function for modulating the value of the variable in the acquired element based on the action history of the element); using the modulation function to modulate the value of the variable per the analysis target (corresponds to mathematical calculations); and generating image data that give a point of coordinates that are values of the modulation result and the objective variable to a coordinate space defined by first and second axes corresponding to the modulation result and the objective variable respectively (corresponds to evaluation and judgement; in particular, a human, with the assistance of pen and paper, can generate image data giving point of coordinates to a coordinate space defined by first and second axes corresponding to the modulation result and the objective variable respectively, the point of coordinates being values of the modulation result and the objective variable).
Step 2A Prong Two Analysis: The judicial exceptions are not integrated into a practical application. In particular, the claim recites additional elements that are mere instructions to apply (See MPEP 2106.05(f)) or insignificant extra-solution activity (See MPEP 2106.05(g)). The limitations:
“a storage unit”
“a modulation unit”
“a generation unit”
As drafted, are additional elements that amount to no more than mere instructions to apply the exception for the abstract ideas. See MPEP 2106.05(f). The limitations:
“stores an analysis target data group which has values of respective variables in a variable group and a value of an objective variable per analysis target and an element group that the variable group and each of one or more modulation method(s) for modulating the variable(s) are set as elements”
As drafted, is an additional element that corresponds to insignificant extra-solution activity. In particular, the additional elements are merely directed towards mere data gathering. See MPEP 2106.05(g). Therefore, the additional elements do not integrate the abstract ideas into a practical application.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, all of the additional elements are “mere instructions to apply an exception” (I.e. the additional elements describe generic units for applying the abstract ideas) or insignificant extra-solution activity (i.e. storing data). Furthermore, the “stores …” limitation is insignificant extra-solution activity that is well-understood, routine, and conventional according to MPEP 2106.05(d) (“The courts have recognized the following computer functions as well‐understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity… iv. Storing and retrieving information in memory). Mere instructions to apply an exception cannot provide an inventive concept. The claim is not patent eligible.
Regarding Claim 2,
Claim 2 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 2 is directed to an apparatus, which is directed to a machine, one of the statutory categories.
Step 2A Prong One Analysis: The limitations:
“selecting the element from the element group on the basis of the action history”
As drafted, under their broadest reasonable interpretations, cover mental processes (concepts performed in the human mind (including an observation, evaluation, judgement, opinion)) and mathematical concepts (mathematical relationships, mathematical formulas or equations, mathematical calculations) but for the recitation of mere instructions to apply language (See MPEP 2106.05(f)) and insignificant extra-solution activity (See MPEP 2106.05(g)). The above limitations in the context of this claim encompass selecting an element from the element group based on the action history (corresponds to evaluation and judgement; in particular, a human, with the assistance of pen and paper, can, based on the action history, select the element from the element group).
Step 2A Prong Two Analysis: The judicial exceptions are not integrated into a practical application. In particular, the claim recites additional elements that are mere instructions to apply (See MPEP 2106.05(f)) or insignificant extra-solution activity (See MPEP 2106.05(g)). The limitations:
“a control unit which generates a control signal … and controls the modulation unit on the basis of the control signal”
As drafted, are additional elements that amount to no more than mere instructions to apply the exception for the abstract ideas. See MPEP 2106.05(f). In addition, the recitation of additional elements in claim 1 of generic units, as drafted, are reciting mere instructions to apply language such that it amounts to no more than mere instructions to apply the exceptions. Furthermore, the “stores …” limitation of claim 1 is an additional element that corresponds to insignificant extra-solution activity as mere data gathering. Therefore, the additional elements do not integrate the abstract ideas into a practical application.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, all of the additional elements are “mere instructions to apply an exception” (I.e. the additional elements describe generic units for applying the abstract ideas) or insignificant extra-solution activity (i.e. storing data). Furthermore, the “stores …” limitation is insignificant extra-solution activity that is well-understood, routine, and conventional according to MPEP 2106.05(d) (“The courts have recognized the following computer functions as well‐understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity… iv. Storing and retrieving information in memory). Mere instructions to apply an exception cannot provide an inventive concept. The claim is not patent eligible.
Regarding Claim 3,
Claim 3 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 3 is directed to an apparatus, which is directed to a machine, one of the statutory categories.
Step 2A Prong One Analysis: The limitations:
“wherein when the image data is generated … selects the element from the element group and newly generates the control signal”
As drafted, under their broadest reasonable interpretations, cover mental processes (concepts performed in the human mind (including an observation, evaluation, judgement, opinion)) and mathematical concepts (mathematical relationships, mathematical formulas or equations, mathematical calculations) but for the recitation of mere instructions to apply language (See MPEP 2106.05(f)) and insignificant extra-solution activity (See MPEP 2106.05(g)). The above limitations in the context of this claim encompass once the image data is generated, selecting the element from the element group and newly generates the control signal (corresponds to evaluation and judgement; in particular, a human, with the assistance of pen and paper, can select the element from the element group and generate a new control signal once the image data is generated).
Step 2A Prong Two Analysis: The judicial exceptions are not integrated into a practical application. In particular, the claim recites additional elements that are mere instructions to apply (See MPEP 2106.05(f)) or insignificant extra-solution activity (See MPEP 2106.05(g)). The limitations:
“by the generation unit”
“the control unit”
As drafted, are additional elements that amount to no more than mere instructions to apply the exception for the abstract ideas. See MPEP 2106.05(f). In addition, the recitation of additional elements in claim 2 of generic units, as drafted, are reciting mere instructions to apply language such that it amounts to no more than mere instructions to apply the exceptions. Furthermore, the “stores …” limitation of claim 2 is an additional element that corresponds to insignificant extra-solution activity as mere data gathering. Therefore, the additional elements do not integrate the abstract ideas into a practical application.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, all of the additional elements are “mere instructions to apply an exception” (I.e. the additional elements describe generic units for applying the abstract ideas) or insignificant extra-solution activity (i.e. storing data). Furthermore, the “stores …” limitation is insignificant extra-solution activity that is well-understood, routine, and conventional according to MPEP 2106.05(d) (“The courts have recognized the following computer functions as well‐understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity… iv. Storing and retrieving information in memory). Mere instructions to apply an exception cannot provide an inventive concept. The claim is not patent eligible.
Regarding Claim 4,
Claim 4 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 4 is directed to an apparatus, which is directed to a machine, one of the statutory categories.
Step 2A Prong One Analysis: The limitations:
“defines the image data as a first state and defines the element group as a first action”
“inputs the image data into a first action value function which outputs a value of the first action in the first state on the basis of a first learning parameter”
“selects a specific element which corresponds to a specific value in the element-based values which are output from the first action value function from the element group”
As drafted, under their broadest reasonable interpretations, cover mental processes (concepts performed in the human mind (including an observation, evaluation, judgement, opinion)) and mathematical concepts (mathematical relationships, mathematical formulas or equations, mathematical calculations) but for the recitation of mere instructions to apply language (See MPEP 2106.05(f)) and insignificant extra-solution activity (See MPEP 2106.05(g)). The above limitations in the context of this claim encompass defining the image data as a first state and the element group as a first action (corresponds to evaluation and judgement; in particular, a human, with the assistance of pen and paper, can define the image data as a first state and the element group as a first action); inputting the image data into a first action value function that outputs a value of the first action in the first state based on a first learning parameter (corresponds to mathematical calculation); and selecting a specific element corresponding to a specific value in the element-based values output from the first action value function from the element group (corresponds to evaluation and judgement; in particular, a human, with the assistance of pen and paper, can select a specific element corresponding to a specific value in the element-based values output from the first action value function from the element group).
Step 2A Prong Two Analysis: The judicial exceptions are not integrated into a practical application. In particular, the claim recites additional elements that are mere instructions to apply (See MPEP 2106.05(f)) or insignificant extra-solution activity (See MPEP 2106.05(g)). The limitations:
“the control unit”
“the control unit … newly generates the control signal which contains the selected specific element and thereby controls the modulation unit”
As drafted, are additional elements that amount to no more than mere instructions to apply the exception for the abstract ideas. See MPEP 2106.05(f). In addition, the recitation of additional elements in claim 3 of generic units, as drafted, are reciting mere instructions to apply language such that it amounts to no more than mere instructions to apply the exceptions. Furthermore, the “stores …” limitation of claim 3 is an additional element that corresponds to insignificant extra-solution activity as mere data gathering. Therefore, the additional elements do not integrate the abstract ideas into a practical application.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, all of the additional elements are “mere instructions to apply an exception” (I.e. the additional elements describe generic units for applying the abstract ideas) or insignificant extra-solution activity (i.e. storing data). Furthermore, the “stores …” limitation is insignificant extra-solution activity that is well-understood, routine, and conventional according to MPEP 2106.05(d) (“The courts have recognized the following computer functions as well‐understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity… iv. Storing and retrieving information in memory). Mere instructions to apply an exception cannot provide an inventive concept. The claim is not patent eligible.
Regarding Claim 5,
Claim 5 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 5 is directed to an apparatus, which is directed to a machine, one of the statutory categories.
Step 2A Prong One Analysis: The limitations:
“wherein the specific value is a value which indicates a maximum value in the element-based values”
As drafted, is part of the abstract idea of claim 4 of selecting a specific element corresponding to a specific value. The limitation of claim 5 further limits the limitation of claim 4 by further defining what the specific value comprises. The above limitation in the context of this claim encompasses selecting a specific element corresponding to a specific value in the element-based values output from the first action value function from the element group, the specific value indicating a maximum value in the element-based values (corresponds to evaluation and judgement; in particular, a human, with the assistance of pen and paper, can select a specific element corresponding to a specific value indicating a maximum value in the element-based values output from the first action value function from the element group).
Step 2A Prong Two Analysis: The judicial exceptions are not integrated into a practical application. In particular, the claim recites additional elements that are mere instructions to apply (See MPEP 2106.05(f)) or insignificant extra-solution activity (See MPEP 2106.05(g)). In addition, the recitation of additional elements in claim 4 of generic units, as drafted, are reciting mere instructions to apply language such that it amounts to no more than mere instructions to apply the exceptions. Furthermore, the “stores …” limitation of claim 4 is an additional element that corresponds to insignificant extra-solution activity as mere data gathering. Therefore, the additional elements do not integrate the abstract ideas into a practical application.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, all of the additional elements are “mere instructions to apply an exception” (I.e. the additional elements describe generic units for applying the abstract ideas) or insignificant extra-solution activity (i.e. storing data). Furthermore, the “stores …” limitation is insignificant extra-solution activity that is well-understood, routine, and conventional according to MPEP 2106.05(d) (“The courts have recognized the following computer functions as well‐understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity… iv. Storing and retrieving information in memory). Mere instructions to apply an exception cannot provide an inventive concept. The claim is not patent eligible.
Regarding Claim 6,
Claim 6 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 6 is directed to an apparatus, which is directed to a machine, one of the statutory categories.
Step 2A Prong One Analysis: The limitations:
“accumulates the image data by the generation unit”
“evaluates the modulation function on the basis of the modulation result and the value of the objective variable”
“selects a combination of time-series first image data and second image data which are accumulated from the accumulation unit”
“defines the image data as a second state and defines the element group as a second action”
“inputs the second image data into a second action value function for outputting a value of the second action in the second state on the basis of a second learning parameter”
“adds a result of evaluation of the modulation function by the evaluation unit to a second output result from the second action value function as a reward and thereby calculates a value of the first action as a teacher signal”
“updates the second learning parameter on the basis of the teacher signal and a first output result which is output in a case where the first image data is input into the second action value function”
“updates the first learning parameter by the updated second learning parameter”
As drafted, under their broadest reasonable interpretations, cover mental processes (concepts performed in the human mind (including an observation, evaluation, judgement, opinion)) and mathematical concepts (mathematical relationships, mathematical formulas or equations, mathematical calculations) but for the recitation of mere instructions to apply language (See MPEP 2106.05(f)) and insignificant extra-solution activity (See MPEP 2106.05(g)). The above limitations in the context of this claim encompass accumulating image data from the generation unit (corresponds to evaluation and judgement; in particular, a human, with the assistance of pen and paper, can accumulate image data); evaluating the modulation function based on the modulation result and the value of the objective variable (corresponds to evaluation and judgement; in particular, a human, with the assistance of pen and paper, can use the modulation result and the value of the objective variable to evaluate the modulation function): selecting a combination of time-series first image data and second image data accumulated by the accumulation unit (corresponds to evaluation and judgement; in particular, a human, with the assistance of pen and paper, can select a combination of accumulated time-series first image data and second image data); defining image data as a second state and the element group as a second action (corresponds to evaluation and judgement; in particular, a human, with the assistance of pen and paper, can define the image data as a second state and the element group as a second action); inputting the second image data into a second action value function which outputs a value of the second action in the second state based on a second learning parameter (corresponds to mathematical calculations); adding a result of evaluation of the modulation function to a second output result from the second action value function as a reward and thereby calculating a value of the first action as a teacher signal (corresponds to mathematical calculation); updating the second learning parameter based on the teacher signal and a first output result that is output when the first image data is input to the second action value function (corresponds to evaluation and judgement; in particular, a human, with the assistance of pen and paper, can update the second learning parameter based on the teacher signal and a first output result from inputting the first image data into the second action value function); and updates the first learning parameter based on the updated second learning parameter (corresponds to evaluation and judgement; in particular, a human, with the assistance of pen and paper, can use the updated second learning parameter to update the first learning parameter).
Step 2A Prong Two Analysis: The judicial exceptions are not integrated into a practical application. In particular, the claim recites additional elements that are mere instructions to apply (See MPEP 2106.05(f)) or insignificant extra-solution activity (See MPEP 2106.05(g)). The limitations:
“accumulation unit”
“an evaluation unit”
“the control unit”
As drafted, are additional elements that amount to no more than mere instructions to apply the exception for the abstract ideas. See MPEP 2106.05(f). In addition, the recitation of additional elements in claim 4 of generic units, as drafted, are reciting mere instructions to apply language such that it amounts to no more than mere instructions to apply the exceptions. Furthermore, the “stores …” limitation of claim 4 is an additional element that corresponds to insignificant extra-solution activity as mere data gathering. Therefore, the additional elements do not integrate the abstract ideas into a practical application.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, all of the additional elements are “mere instructions to apply an exception” (I.e. the additional elements describe generic units for applying the abstract ideas) or insignificant extra-solution activity (i.e. storing data). Furthermore, the “stores …” limitation is insignificant extra-solution activity that is well-understood, routine, and conventional according to MPEP 2106.05(d) (“The courts have recognized the following computer functions as well‐understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity… iv. Storing and retrieving information in memory). Mere instructions to apply an exception cannot provide an inventive concept. The claim is not patent eligible.
Regarding Claim 7,
Claim 7 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 7 is directed to an apparatus, which is directed to a machine, one of the statutory categories.
Step 2A Prong One Analysis: The limitations:
“evaluates the modulation function on the basis of the values of the modulation result and the objective variable”
As drafted, under their broadest reasonable interpretations, cover mental processes (concepts performed in the human mind (including an observation, evaluation, judgement, opinion)) and mathematical concepts (mathematical relationships, mathematical formulas or equations, mathematical calculations) but for the recitation of mere instructions to apply language (See MPEP 2106.05(f)) and insignificant extra-solution activity (See MPEP 2106.05(g)). The above limitations in the context of this claim encompass evaluating the modulation function based on the modulation result and the value of the objective variable (corresponds to evaluation and judgement; in particular, a human, with the assistance of pen and paper, can use the modulation result and the value of the objective variable to evaluate the modulation function).
Step 2A Prong Two Analysis: The judicial exceptions are not integrated into a practical application. In particular, the claim recites additional elements that are mere instructions to apply (See MPEP 2106.05(f)) or insignificant extra-solution activity (See MPEP 2106.05(g)). The limitations:
“an evaluation unit”
“an output unit”
As drafted, are additional elements that amount to no more than mere instructions to apply the exception for the abstract ideas. See MPEP 2106.05(f). The limitations:
“outputs the image data to be displayable in a case where a result of evaluation of the modulation function by the evaluation unit is more than a target value”
As drafted, are additional elements that correspond to insignificant extra-solution activity. In particular, the additional elements are merely directed towards mere data gathering. See MPEP 2106.05(g). In addition, the recitation of additional elements in claim 1 of generic units, as drafted, are reciting mere instructions to apply language such that it amounts to no more than mere instructions to apply the exceptions. Furthermore, the “stores …” limitation of claim 1 is an additional element that corresponds to insignificant extra-solution activity as mere data gathering. Therefore, the additional elements do not integrate the abstract ideas into a practical application.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, all of the additional elements are “mere instructions to apply an exception” (I.e. the additional elements describe generic units for applying the abstract ideas) or insignificant extra-solution activity (i.e. outputting/transmitting data and storing data). Furthermore, the “stores …” limitation is insignificant extra-solution activity that is well-understood, routine, and conventional according to MPEP 2106.05(d) (“The courts have recognized the following computer functions as well‐understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity… i. Receiving or transmitting data over a network … iv. Storing and retrieving information in memory). Mere instructions to apply an exception cannot provide an inventive concept. The claim is not patent eligible.
Regarding Claim 8,
Claim 8 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 8 is directed to an apparatus, which is directed to a machine, one of the statutory categories.
Step 2A Prong One Analysis: The limitations:
“generates a regression model for regressing the first-axis coordinate value which corresponds to the modulation result with the objective variable and outputs the accuracy of the regression model as a result of evaluation of the modulation function”
As drafted, under their broadest reasonable interpretations, cover mental processes (concepts performed in the human mind (including an observation, evaluation, judgement, opinion)) and mathematical concepts (mathematical relationships, mathematical formulas or equations, mathematical calculations) but for the recitation of mere instructions to apply language (See MPEP 2106.05(f)) and insignificant extra-solution activity (See MPEP 2106.05(g)). The above limitations in the context of this claim encompass generating a regression model for regressing a first-axis coordinate value correspond to the modulation result with the objective variable an outputs an accuracy of the regression model as a result of the evaluation of the modulation function (corresponds to evaluation and judgement; in particular, a human, with the assistance of pen and paper, can generate a regression model for regressing the first-axis coordinate value corresponding to the modulation result with the objective variable and outputting the accuracy as a result of the evaluation of the modulation function).
Step 2A Prong Two Analysis: The judicial exceptions are not integrated into a practical application. In particular, the claim recites additional elements that are mere instructions to apply (See MPEP 2106.05(f)) or insignificant extra-solution activity (See MPEP 2106.05(g)). The limitations:
“the evaluation unit”
As drafted, are additional elements that amount to no more than mere instructions to apply the exception for the abstract ideas. See MPEP 2106.05(f). The limitations:
“wherein the objective variable is a quantitative variable”
As drafted, is an additional element that is part of the insignificant extra-solution activity of claim 6 of storing an analysis target data group which has values of respective variables in a variable group and a value of an objective variable per analysis target. The limitation of claim 8 further limits the limitation of claim 6 by further defining what the objective variable comprises. In addition, the recitation of additional elements in claim 6 of generic units, as drafted, are reciting mere instructions to apply language such that it amounts to no more than mere instructions to apply the exceptions. Furthermore, the “stores …” limitation of claim 6 is an additional element that corresponds to insignificant extra-solution activity as mere data gathering. Therefore, the additional elements do not integrate the abstract ideas into a practical application.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, all of the additional elements are “mere instructions to apply an exception” (I.e. the additional elements describe generic units for applying the abstract ideas) or insignificant extra-solution activity (i.e. storing data). Furthermore, the “stores …” limitation is insignificant extra-solution activity that is well-understood, routine, and conventional according to MPEP 2106.05(d) (“The courts have recognized the following computer functions as well‐understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity… iv. Storing and retrieving information in memory). Mere instructions to apply an exception cannot provide an inventive concept. The claim is not patent eligible.
Regarding Claim 9,
Claim 9 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 9 is directed to an apparatus, which is directed to a machine, one of the statutory categories.
Step 2A Prong One Analysis: The limitations:
“generates an identification model which identifies the first-axis coordinate value which corresponds to the modulation result with the objective variable and outputs the accuracy of the identification model as a result of evaluation of the modulation function”
As drafted, under their broadest reasonable interpretations, cover mental processes (concepts performed in the human mind (including an observation, evaluation, judgement, opinion)) and mathematical concepts (mathematical relationships, mathematical formulas or equations, mathematical calculations) but for the recitation of mere instructions to apply language (See MPEP 2106.05(f)) and insignificant extra-solution activity (See MPEP 2106.05(g)). The above limitations in the context of this claim encompass generating an identification model for identifying a first-axis coordinate value correspond to the modulation result with the objective variable an outputs an accuracy of the identification model as a result of the evaluation of the modulation function (corresponds to evaluation and judgement; in particular, a human, with the assistance of pen and paper, can generate an identification model for identifying the first-axis coordinate value corresponding to the modulation result with the objective variable and outputting the accuracy as a result of the evaluation of the modulation function).
Step 2A Prong Two Analysis: The judicial exceptions are not integrated into a practical application. In particular, the claim recites additional elements that are mere instructions to apply (See MPEP 2106.05(f)) or insignificant extra-solution activity (See MPEP 2106.05(g)). The limitations:
“the evaluation unit”
As drafted, are additional elements that amount to no more than mere instructions to apply the exception for the abstract ideas. See MPEP 2106.05(f). The limitations:
“wherein the objective variable is information for classification of the analysis target data group”
As drafted, is an additional element that is part of the insignificant extra-solution activity of claim 6 of storing an analysis target data group which has values of respective variables in a variable group and a value of an objective variable per analysis target. The limitation of claim 9 further limits the limitation of claim 6 by further defining what the objective variable comprises. In addition, the recitation of additional elements in claim 6 of generic units, as drafted, are reciting mere instructions to apply language such that it amounts to no more than mere instructions to apply the exceptions. Furthermore, the “stores …” limitation of claim 6 is an additional element that corresponds to insignificant extra-solution activity as mere data gathering. Therefore, the additional elements do not integrate the abstract ideas into a practical application.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, all of the additional elements are “mere instructions to apply an exception” (I.e. the additional elements describe generic units for applying the abstract ideas) or insignificant extra-solution activity (i.e. storing data). Furthermore, the “stores …” limitation is insignificant extra-solution activity that is well-understood, routine, and conventional according to MPEP 2106.05(d) (“The courts have recognized the following computer functions as well‐understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity… iv. Storing and retrieving information in memory). Mere instructions to apply an exception cannot provide an inventive concept. The claim is not patent eligible.
Regarding Claim 10,
Claim 10 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 10 is directed to a method, which is directed to a process, one of the statutory categories.
Step 2A Prong One Analysis: The limitations:
“a modulation process of, when the element which is selected from the element group is acquired, planning a modulation function for modulating a value of the variable which is contained in the acquired element on the basis of an action history which is the history of the acquired element”
“modulating the value of the variable per the analysis target on the basis of the modulation function”
“a generation process of generating image data which gives a point of coordinates which are values of the modulation result and the objective variable to a coordinate space which is defined by a first axis which corresponds to a result of modulation by the modulation process and a second axis which corresponds to the objective variable, per the analysis target”
As drafted, under their broadest reasonable interpretations, cover mental processes (concepts performed in the human mind (including an observation, evaluation, judgement, opinion)) and mathematical concepts (mathematical relationships, mathematical formulas or equations, mathematical calculations) but for the recitation of mere instructions to apply language (See MPEP 2106.05(f)) and insignificant extra-solution activity (See MPEP 2106.05(g)). The above limitations in the context of this claim encompass when a selected element is acquired, planning a modulation function for modulating the value of the variable in the acquired element based on an action history of the acquired element (corresponds to evaluation and judgement; in particular, a human, with the assistance of pen and paper, can plan a modulation function for modulating the value of the variable in the acquired element based on the action history of the element); using the modulation function to modulate the value of the variable per the analysis target (corresponds to mathematical calculations); and generating image data that give a point of coordinates that are values of the modulation result and the objective variable to a coordinate space defined by first and second axes corresponding to the modulation result and the objective variable respectively (corresponds to evaluation and judgement; in particular, a human, with the assistance of pen and paper, can generate image data giving point of coordinates to a coordinate space defined by first and second axes corresponding to the modulation result and the objective variable respectively, the point of coordinates being values of the modulation result and the objective variable).
Step 2A Prong Two Analysis: The judicial exceptions are not integrated into a practical application. In particular, the claim recites additional elements that are mere instructions to apply (See MPEP 2106.05(f)) or insignificant extra-solution activity (See MPEP 2106.05(g)). The limitations:
“a data processing apparatus which has a processor which executes a program and a storage device which stores the program”
“the processor”
As drafted, are additional elements that amount to no more than mere instructions to apply the exception for the abstract ideas. See MPEP 2106.05(f). The limitation:
“wherein the data processing apparatus is accessible to an analysis target data group which has values of respective variables in a variable group and a value of an objective variable per analysis target and to an element group which defines the variable group and each of one or more modulation method(s) for modulating the variable(s) as elements”
As drafted, is an additional element that corresponds to insignificant extra-solution activity. In particular, the additional elements are merely directed towards mere data gathering. See MPEP 2106.05(g). Therefore, the additional elements do not integrate the abstract ideas into a practical application.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, all of the additional elements are “mere instructions to apply an exception” (I.e. the additional elements describe generic apparatus, processor, and memory for applying the abstract ideas) or insignificant extra-solution activity (i.e. accessing/retrieving data). Furthermore, the “… is accessible …” limitation is insignificant extra-solution activity that is well-understood, routine, and conventional according to MPEP 2106.05(d) (“The courts have recognized the following computer functions as well‐understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity… iv. Storing and retrieving information in memory). Mere instructions to apply an exception cannot provide an inventive concept. The claim is not patent eligible.
Regarding Claim 11,
Claim 11 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 11, as noted above, is directed towards non-statutory subject matter as software per se. However, for purposes of this rejection, it will be assumed that the claim is directed to a non-transitory medium that stores the data processing program and that the claim is therefore directed to an article of manufacture, one of the statutory categories.
Step 2A Prong One Analysis: The limitations:
“a modulation process of, when the element which is selected from the element group is acquired, planning a modulation function for modulating a value of the variable which is contained in the acquired element on the basis of an action history which is the history of the acquired element”
“modulating the value of the variable per the analysis target on the basis of the modulation function”
“a generation process of generating image data which gives a point of coordinates which are values of the modulation result and the objective variable to a coordinate space which is defined by a first axis which corresponds to a result of modulation by the modulation process and a second axis which corresponds to the objective variable, per the analysis target”
As drafted, under their broadest reasonable interpretations, cover mental processes (concepts performed in the human mind (including an observation, evaluation, judgement, opinion)) and mathematical concepts (mathematical relationships, mathematical formulas or equations, mathematical calculations) but for the recitation of mere instructions to apply language (See MPEP 2106.05(f)) and insignificant extra-solution activity (See MPEP 2106.05(g)). The above limitations in the context of this claim encompass when a selected element is acquired, planning a modulation function for modulating the value of the variable in the acquired element based on an action history of the acquired element (corresponds to evaluation and judgement; in particular, a human, with the assistance of pen and paper, can plan a modulation function for modulating the value of the variable in the acquired element based on the action history of the element); using the modulation function to modulate the value of the variable per the analysis target (corresponds to mathematical calculations); and generating image data that give a point of coordinates that are values of the modulation result and the objective variable to a coordinate space defined by first and second axes corresponding to the modulation result and the objective variable respectively (corresponds to evaluation and judgement; in particular, a human, with the assistance of pen and paper, can generate image data giving point of coordinates to a coordinate space defined by first and second axes corresponding to the modulation result and the objective variable respectively, the point of coordinates being values of the modulation result and the objective variable).
Step 2A Prong Two Analysis: The judicial exceptions are not integrated into a practical application. In particular, the claim recites additional elements that are mere instructions to apply (See MPEP 2106.05(f)) or insignificant extra-solution activity (See MPEP 2106.05(g)). The limitations:
“A data processing program”
“a processor”
As drafted, are additional elements that amount to no more than mere instructions to apply the exception for the abstract ideas. See MPEP 2106.05(f). The limitation:
“a processor which is accessible to an analysis target data group which has values of respective variables in a variable group and a value of an objective variable per analysis target and to an element group which defines the variable group and each of one or more modulation method(s) of modulating the variable(s) as elements”
As drafted, is an additional element that corresponds to insignificant extra-solution activity. In particular, the additional elements are merely directed towards mere data gathering. See MPEP 2106.05(g). Therefore, the additional elements do not integrate the abstract ideas into a practical application.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, all of the additional elements are “mere instructions to apply an exception” (I.e. the additional elements describe generic program instructions and a processor for applying the abstract ideas) or insignificant extra-solution activity (i.e. accessing/retrieving data). Furthermore, the “… is accessible …” limitation is insignificant extra-solution activity that is well-understood, routine, and conventional according to MPEP 2106.05(d) (“The courts have recognized the following computer functions as well‐understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity… iv. Storing and retrieving information in memory). Mere instructions to apply an exception cannot provide an inventive concept. The claim is not patent eligible.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1-11 are rejected under 35 U.S.C. 103 as being unpatentable over Shibahara et al. (US 2021/0074428 A1) in view of Kubalik et al. ("Symbolic Regression Methods for Reinforcement Learning").
Regarding Claim 1,
Shibahara et al. teaches a data processing apparatus ([0022]: "An example of a data processing apparatus, a data analysis method, and a data analysis program according to a first embodiment will be described hereinafter with reference to the accompanying drawings" teaches a data processing apparatus. Fig. 2; [0033]: "FIG. 2 is a block diagram depicting an example of a hardware configuration of the data processing apparatus 100. The data processing apparatus 100 has a processor 201, a storage device 202, an input device 203, an output device 204, a communication interface (communication IF) 205, and an image processing circuit 207" teaches the data processing apparatus having a processor and storage device) having:
a storage unit which stores an analysis target data group which has values of respective variables in a variable group and a value of an objective variable per analysis target and an element group that the variable group and each of one or more modulation method(s) for modulating the variable(s) are set as elements (Fig 1; Fig. 2; [0033]-[0035]: "FIG. 2 is a block diagram depicting an example of a hardware configuration of the data processing apparatus 100. The data processing apparatus 100 has a processor 201, a storage device 202, an input device 203, an output device 204, a communication interface (communication IF) 205, and an image processing circuit 207 … The processor 201 controls the data processing apparatus 100. The storage device 202 serves as a work area for the processor 201. Furthermore, the storage device 202 is a non-transitory or transitory recording medium storing various programs and data and the object-to-be-analyzed DB … The image processing circuit 207 executes a series of processing (1) to (6) depicted in FIG. 1 while referring to a pattern table 208. The pattern table 208 is stored, for example, in a memory area, not depicted, within the image processing circuit 207. It is noted that while the image processing circuit 207 is realized by the circuit configuration, the image processing circuit 207 may be realized by causing the processor 201 to execute programs stored in the storage device 202" teaches a storage device 202 (storage unit) for storing the object-to-be-analyzed DB 104 (analysis target data group) and a pattern table 208. Fig. 3; [0036]-[0038]: "FIG. 3 is an explanatory diagram depicting an example of the object-to-be-analyzed DB 104. The object-to-be-analyzed DB 104 has a patient ID 301, an objective variable 302, and a factor group 303 as fields … Furthermore, a modulation method 304 is associated with each factor in the factor group 303. The modulation method 304 is an operator with the value of a factor as an operand" teaches that the object-to-be-analyzed DB 104 (analysis target data group) that has an objective variable 302 (value of an objective variable per analysis target), and a factor group 303 (variable group) which has values of respective factors (variables). Fig. 4; [0039]-[0041]: "FIG. 4 is an explanatory diagram depicting an example of the pattern table 208. The pattern table 208 is a table that specifies the element group 105 used in generating a control signal for formulating the equations 111 and 112 and plotting the coordinate values onto the coordinate space 110 … The pattern table 208 has a control ID 401 and an element number sequence 402 as fields. … The element number sequence 402 is a set of element numbers corresponding to elements selectable by each module identified by the control ID 401 … the elements in the pattern table 208 of FIG. 4 include the types of the factors and the types of the modulation methods" teaches the pattern table 208 specifies an element group 105 with the factors (variable group) and modulation methods for modulating the variables as elements);
a modulation unit which when the element which is selected from the element group is acquired, plans a modulation function of modulating the value of the variable which is contained in the acquired element … (Fig. 5; [0042]-[0046]: "FIG. 5 is a block diagram depicting an example of a circuit configuration of the image processing circuit 207. The image processing circuit 207 has a data memory 500, the X-axis modulation unit 510, the Y-axis modulation unit 520, an image generator 530, an evaluator 540, a controller 550, and the pattern table 208. … The X-axis modulation unit 510 configures part of the equation formulation AI 101 depicted in FIG. 1. The X-axis modulation unit 510 sets factors and modulation methods in the X-axis equation 111. … The multiplexer 513 selects a factor x1 from a control signal output from the controller 550. The multiplexer 513 may receive selection of the factor x1 selected by the user … The modulator 515 selects a modulation method opx1 from the control signal output from the controller 550. The modulator 515 applies the modulation method opx1 to all cases related to the factor x1" teaches a modulation unit 510 that plans a modulation method (modulation function) for modulating a value of a selected factor (value of the variable which is contained in the acquired element)) and
modulates the value of the variable per the analysis target on the basis of the modulation function (Fig. 5; [0046]-[0047]: "The modulator 515 selects a modulation method opx1 from the control signal output from the controller 550. The modulator 515 applies the modulation method opx1 to all cases related to the factor x1 … Examples of the modulation method opx1 to be applied include the non-modulation, the sign change, logarithmic transformation (for example, log.sub.10), absolute value transformation, and exponentiation" teaches that the modulation method (modulation function) is applied to the factor (value of the variable per the analysis target) to modulate the factor); and
a generation unit which generates image data which gives a point of coordinates which are values of the modulation result and the objective variable to a coordinate space which is defined by a first axis which corresponds to a result of modulation by the modulation unit and a second axis which corresponds to the objective variable per the analysis target (Fig. 5; [0055]-[0056]: "The image generator 530 configures part of the equation formulation AI 101 depicted in FIG. 1. The image generator 530 receives the signals x′ and y′ output from the X-axis modulation unit 510 and the Y-axis modulation unit 520. The signal x′ is a set of x coordinate values (one-dimensional vector) calculated from the X-axis equation 111 per case, while the signal y′ is a set of y coordinate values (one-dimensional vector) calculated from the Y-axis equation 112 per case. The image generator 530 plots the coordinate values at the same locations within the signals x′ and y′ onto the coordinate space 110, thereby rendering pixels that configure the image data I about the coordinate space 110 onto which the patient data is plotted … At that time, the image generator 530 determines a color of each pixel by referring to the objective variable 302 on the data memory 500 … The image generator 530 stores the generated image data I in the data memory 500 and outputs the image data I to the controller 550" teaches an image generator 530 (generation unit) that generates image data which gives coordinate points that are values of modulation outputs (modulation results) and the objective variable to a coordinate space, with axes corresponding to a modulation output (modulation result) from the modulation unit and the objective variable).
Shibahara et al. does not appear to explicitly teach plans a modulation function of modulating the value of the variable which is contained in the acquired element on the basis of an action history which is the history of the acquired element.
However, Kubalik et al. teaches plans a modulation function of modulating the value of the variable which is contained in the acquired element on the basis of an action history which is the history of the acquired element (Fig. 1; Algorithm 1; Section III, D: "In symbolic value iteration (SVI), the optimal value function is found iteratively, just like in standard value iteration [32]. In each iteration l, the value function Vl−1 from the previous iteration is used to compute the target for improving the value function Vl in the current iteration. For each state xi ∈ X, the target ti,l ∈ R is calculated by evaluating the right-hand-side of (5):
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Here, the maximization is carried out over the predefined discrete control action set U. Note that virtually all control systems use discrete control actions … In addition, as the next states and rewards are pre-computed and provided to the SVI algorithm in the data set D (8), we can replace (10) by its computationally more efficient equivalent:
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Given the target ti,l, an improved value function V` is constructed by applying SR [Symbolic Regression] with the following fitness function:
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This fitness function is again the mean-squared Bellman error. However, as opposed to (9), the above criterion (12) defines a true regression problem: the target to be fitted is fixed as it is based on Vl−1 from the previous iteration" teaches a symbolic value iteration wherein the modulations for the value function for the target (selected) element are planned based on the set of discrete control actions (action history) over time (e.g. the mathematical symbol modulation planned for the value function is based on the discrete control actions)).
Shibahara et al. and Kubalik et al. are analogous to the claimed invention because they are directed towards machine learning data processing using reinforcement learning.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate plans a modulation function of modulating the value of the variable which is contained in the acquired element on the basis of an action history which is the history of the acquired element as taught by Kubalik et al. to the claimed invention of Shibahara et al.
One of ordinary skill in the art would have been motivated to make this modification to produce "value functions [that] yield well-performing policies and are compact, mathematically tractable, and easy to plug into other algorithms" (Kubalik et al. Abstract).
Regarding Claim 2,
Shibahara et al. in view of Kubalik et al. teaches the data processing apparatus of claim 1.
In addition, Shibahara et al. further teaches having: a control unit which generates a control signal for selecting the element from the element group on the basis of the action history and controls the modulation unit on the basis of the control signal (Fig. 5; [0042]-[0046]: "FIG. 5 is a block diagram depicting an example of a circuit configuration of the image processing circuit 207. The image processing circuit 207 has a data memory 500, the X-axis modulation unit 510, the Y-axis modulation unit 520, an image generator 530, an evaluator 540, a controller 550, and the pattern table 208 … The multiplexer 513 selects a factor x1 from a control signal output from the controller 550. The multiplexer 513 may receive selection of the factor x1 selected by the user … The modulator 515 selects a modulation method opx1 from the control signal output from the controller 550" teaches a controller 550 (control unit) that outputs (generates) a control signal for selecting a factor (element) for the modulation unit and for controlling the modulation unit. Fig. 7; [0073]: "FIG. 7 is an explanatory diagram depicting an example of the control signal a(t). The control signal a(t) has a control ID 401 and an action 701 as fields. Each action 701 indicates selection of a factor or a modulation method by the X-axis modulation unit 510" teaches that the control signal of the control unit selects a factor (element) based on an action 701 (action history) and controls a modulation method for the modulation unit based on the control signal in accordance with the action 701 (action history)).
Regarding Claim 3,
Shibahara et al. in view of Kubalik et al. teaches the data processing apparatus of claim 2.
In addition, Shibahara et al. further teaches wherein when the image data is generated by the generation unit, the control unit selects the element from the element group and newly generates the control signal (Fig. 5; [0062]: "the controller 550 controls the X-axis modulation unit 510 and the Y-axis modulation unit 520. Specifically, when the image data I(t) is input to the controller 550 from the image generator 530, the controller 550 generates the control signal a(t) for controlling the X-axis modulation unit 510 and the Y-axis modulation unit 520 and controls generation of image data I (t+1) in a next time step (t+1)" teaches that when the image data is generated by the image generator 530 (generation unit), the controller 550 (control unit) receives the image data and generates a new control signal for controlling the modulation unit (e.g. selects the factor (element) to generate the control signal for the modulation unit)).
Regarding Claim 4,
Shibahara et al. in view of Kubalik et al. teaches the data processing apparatus of claim 3.
In addition, Shibahara et al. further teaches wherein the control unit defines the image data as a first state and defines the element group as a first action (Fig. 5; Fig. 6; [0153]: "the controller 550 may include the Q* network 601 that outputs the one-dimensional array z(t) indicating the value of each element in the pattern table 208 in a case of taking a first action in a first state on the basis of the learning parameter θ* when the image data I(t+1) is assumed as the first state and a first element group contained in the control signal a(t) is assumed as the first action" teaches the controller 550 (control unit) defines the image data as a first state and the element group as a first action),
inputs the image data into a first action value function which outputs a value of the first action in the first state on the basis of a first learning parameter (Fig. 5; Fig. 6; [0153]: "the controller 550 may include the Q* network 601 that outputs the one-dimensional array z(t) indicating the value of each element in the pattern table 208 in a case of taking a first action in a first state on the basis of the learning parameter θ* when the image data I(t+1) is assumed as the first state and a first element group contained in the control signal a(t) is assumed as the first action" teaches the image data as a first state being input to a Q* network 601 (first action value function) that outputs a value of the first action in a first state based on the learning parameter θ* (first learning parameter). Fig. 5; Fig. 6; [0064]: "The Q* network 601 and the Q network 602 are action value functions identical in configuration for learning the control signal a(t) that is an action to maximize a value" teaches that the Q* network is an action value function),
selects a specific element which corresponds to a specific value in the element-based values which are output from the first action value function from the element group (Fig. 5; Fig. 6; [0153]: "the controller 550 may include the Q* network 601 that outputs the one-dimensional array z(t) indicating the value of each element in the pattern table 208 in a case of taking a first action in a first state on the basis of the learning parameter θ* when the image data I(t+1) is assumed as the first state and a first element group contained in the control signal a(t) is assumed as the first action, update an element … in the control signal a(t), the element corresponding to a specific value … in the one-dimensional array z(t) indicating the value of each element in the pattern table 208, to a specific element … corresponding to the specific value … in the pattern table 208" teaches selecting a specific element corresponding to a specific value in the one-dimensional array indicating the values of each element (element-based values which are output from the first action value function)),
newly generates the control signal which contains the selected specific element and thereby controls the modulation unit (Fig. 5; Fig. 6; [0153]: "update an element … in the control signal a(t), the element corresponding to a specific value … in the one-dimensional array z(t) indicating the value of each element in the pattern table 208, to a specific element … corresponding to the specific value … in the pattern table 208, and control the X-axis modulation unit 510 and the Y-axis modulation unit 520 on the basis of the updated control signal a(t)" teaches that the control signal is updated (newly generated) with the specific element to control the modulation unit).
Regarding Claim 5,
Shibahara et al. in view of Kubalik et al. teaches the data processing apparatus of claim 4.
In addition, Shibahara et al. further teaches wherein the specific value is a value which indicates a maximum value in the element-based values (Fig. 5; Fig. 6; [0155]: "the specific value may be a value indicating a maximum value in the one-dimensional array z(t) indicating the value of each element in the pattern table 208" teaches that the specific value is a value indicating the maximum value in the one-dimensional array indicating the values of each element (element-based values)).
Regarding Claim 6,
Shibahara et al. in view of Kubalik et al. teaches the data processing apparatus of claim 4.
In addition, Shibahara et al. further teaches having: an accumulation unit which accumulates the image data by the generation unit (Fig. 5; Fig. 6; [0069]: "The replay memory 620 stores a data pack D(t). The data pack D(t) contains the statistic r(t), the image data I(t) and I(t+1)), the control signal a(t), and the stop signal K(t) in the time step t" teaches a replay memory 620 (accumulation unit) that stores (accumulates) the image data from the image generator 530 (generation unit)); and
an evaluation unit which evaluates the modulation function on the basis of the modulation result and the value of the objective variable (Fig. 5; [0057]-[0058]: "The evaluator 540 has the discriminator 102 depicted in FIG. 1. The evaluator 540 acquires the signals x′ and y′ output from the X-axis modulation unit 510 and the Y-axis modulation unit 520 and the objective variables 302 from the data memory 500. The evaluator 540 calculates statistics r(t) in a time step t (where t is an integer equal to or greater than 1) in response to types of the objective variables 302 … Specifically, the evaluator 540 executes, for example, the discriminator 102, thereby calculating the statistics r(t) indicating the prediction precision for predicting the response or the non-response per patient" teaches an evaluator 540 (evaluation unit) that acquires the output of the modulation unit (modulation result) and the value of the objective variable to evaluate the modulation method (modulation function)),
wherein the control unit selects a combination of time-series first image data and second image data which are accumulated from the accumulation unit (Fig. 5; Fig. 6; [0069]: "The replay memory 620 stores a data pack D(t). The data pack D(t) contains the statistic r(t), the image data I(t) and I(t+1)), the control signal a(t), and the stop signal K(t) in the time step t. In the data pack D(t), a state of a time step t+1 generated in the case of taking the action (control signal a(t)) in the state (image data I(t)) in the time step t is the image data I(t+1), and the reward obtained in the case of taking the action (control signal a(t)) is the statistics r(t); thus, the data pack D(t) identifies whether to continue to generate the image data I(t) and I(t+1) in the next time step t=t+1 (stop signal K(t))" teaches the controller 550 (control unit) that selects time-step image data I(t) and I(t+1) (combination of time-series first image data and second image data) that are accumulated with the replay memory 620 (accumulation unit)),
defines the image data as a second state and defines the element group as a second action (Fig. 5; Fig. 6; [0157]: "The controller 550 includes the Q network 602 that outputs the one-dimensional array z(t) indicating the value of each element in the pattern table 208 in a case of taking a second action in a second state on the basis of the learning parameter θ when input image data is assumed as the second state and a second element group contained in the updated control signal a(t) is assumed as the second action" teaches the controller 550 (control unit) defines the image data as a second state and the element group as a second action),
inputs the second image data into a second action value function for outputting a value of the second action in the second state on the basis of a second learning parameter (Fig. 5; Fig. 6; [0157]: "The controller 550 includes the Q network 602 that outputs the one-dimensional array z(t) indicating the value of each element in the pattern table 208 in a case of taking a second action in a second state on the basis of the learning parameter θ when input image data is assumed as the second state and a second element group contained in the updated control signal a(t) is assumed as the second action" teaches the image data as a second state being input to a Q network 602 (second action value function) that outputs a value of the second action in a second state based on the learning parameter θ (second learning parameter). Fig. 5; Fig. 6; [0064]: "The Q* network 601 and the Q network 602 are action value functions identical in configuration for learning the control signal a(t) that is an action to maximize a value" teaches that the Q network is an action value function),
adds a result of evaluation of the modulation function by the evaluation unit to a second output result from the second action value function as a reward and thereby calculates a value of the first action as a teacher signal (Fig. 5; Fig. 6; [0157]: "The controller 550 may calculate a value of the first action as the supervisory data y(j) by adding, as a reward, statistics r(j) that is an evaluation result by the evaluator 540 to an output result in a case of inputting the image data I(t+1) to the Q network 602" teaches that the evaluation result from the evaluator 540 (evaluation unit) is added, as a reward, to an output result (second output result) of the Q network 602 (second action value function) to calculate a value of the first action as a supervisory signal (teacher signal)),
updates the second learning parameter on the basis of the teacher signal and a first output result which is output in a case where the first image data is input into the second action value function and updates the first learning parameter by the updated second learning parameter (Fig. 5; Fig. 6; [0157]: "update the learning parameter θ on the basis of the supervisory data y(j) and an output result in a case of inputting the image data I(t) to the Q network 602, and update the learning parameter θ* to the updated learning parameter θ" teaches that the learning parameter θ (second learning parameter) is updated based on the supervisory signal (teacher signal) and an output result (first output result) of the Q network 602 (second action value function) when input data I(t) (first image data) is input into the Q network 602 and updating the learning parameter θ* (first learning parameter) by the updated learning parameter θ (second learning parameter)).
Regarding Claim 7,
Shibahara et al. in view of Kubalik et al. teaches the data processing apparatus of claim 1.
In addition, Shibahara et al. further teaches having: an evaluation unit which evaluates the modulation function on the basis of the values of the modulation result and the objective variable (Fig. 2; Fig. 5; [0159]: "the data processing apparatus 100 includes: the evaluator 540; and an output section (output device 204 or communication IF 205). The evaluator 540 may evaluate the objective variable 302 on the basis of the first modulation result …, the second modulation result …, and the information … associated with the objective variable 302" teaches an evaluator 540 (evaluation unit) that acquires the output of the modulation unit (modulation result) and the value of the objective variable to evaluate the modulation method (modulation function)); and
an output unit which outputs the image data to be displayable in a case where a result of evaluation of the modulation function by the evaluation unit is more than a target value (Fig. 2; Fig. 5; [0159]: "the data processing apparatus 100 includes: the evaluator 540; and an output section (output device 204 or communication IF 205) … The output section may output image data I(j) in a displayable fashion in a case in which the statistics r(j) that is the evaluation result by the evaluator 540 is, for example, equal to or greater than the target value input to the target value input area 862" teaches an output section (output unit) to output the image data to be displayable when a result of the evaluation by the evaluator 540 (evaluation unit) is greater than a target value).
Regarding Claim 8,
Shibahara et al. in view of Kubalik et al. teaches the data processing apparatus of claim 6.
In addition, Shibahara et al. further teaches wherein the objective variable is a quantitative variable ([0163]: "the objective variable 302 may be the quantitative variable" teaches that the objective variable is a quantitative variable), and
the evaluation unit generates a regression model for regressing the first-axis coordinate value which corresponds to the modulation result with the objective variable and outputs the accuracy of the regression model as a result of evaluation of the modulation function (Fig. 5; Fig. 6; [0159]: "the data processing apparatus 100 includes: the evaluator 540; and an output section (output device 204 or communication IF 205). The evaluator 540 may evaluate the objective variable 302 on the basis of the first modulation result (X coordinate value of each patient data), the second modulation result …, and the information … associated with the objective variable 302. The output section may output image data I(j) in a displayable fashion in a case in which the statistics r(j) that is the evaluation result by the evaluator 540 is, for example, equal to or greater than the target value input to the target value input area 862" teaches that the evaluator 540 (evaluation unit) outputs the statistics of the evaluation result (e.g. accuracy) based on the first axis coordinate value corresponding to the modulation result and the objective variable. Fig. 5; [0059]: "A logistic regression unit, a linear regression unit, a neural network unit, a gradient boosting unit are mounted as regression calculation units as well as the discriminator 102 in the evaluator 540. The evaluator 540 stores the statistics r(t) in the data memory 500 and outputs the statistics r(t) to the controller 550" teaches that the evaluator comprises a regression unit for performing a regression calculation with a regression model to generate the evaluation statistics (e.g. accuracy of regression model)).
Regarding Claim 9,
Shibahara et al. in view of Kubalik et al. teaches the data processing apparatus of claim 6.
In addition, Shibahara et al. further teaches wherein the objective variable is information for classification of the analysis target data group ([0161]: "the objective variable 302 may be information for classifying the object-to-be-analyzed data group" teaches that the objective variable is information for classifying the object-to-be-analyzed data group (analysis target data group)), and
the evaluation unit generates an identification model which identifies the first-axis coordinate value which corresponds to the modulation result with the objective variable and outputs the accuracy of the identification model as a result of evaluation of the modulation function (Fig. 5; Fig. 6; [0159]: "the data processing apparatus 100 includes: the evaluator 540; and an output section (output device 204 or communication IF 205). The evaluator 540 may evaluate the objective variable 302 on the basis of the first modulation result (X coordinate value of each patient data), the second modulation result …, and the information … associated with the objective variable 302. The output section may output image data I(j) in a displayable fashion in a case in which the statistics r(j) that is the evaluation result by the evaluator 540 is, for example, equal to or greater than the target value input to the target value input area 862" teaches that the evaluator 540 (evaluation unit) outputs the statistics of the evaluation result (e.g. accuracy) based on the first axis coordinate value corresponding to the modulation result and the objective variable. Fig. 5; [0059]: "A logistic regression unit, a linear regression unit, a neural network unit, a gradient boosting unit are mounted as regression calculation units as well as the discriminator 102 in the evaluator 540. The evaluator 540 stores the statistics r(t) in the data memory 500 and outputs the statistics r(t) to the controller 550" teaches that the evaluator comprises a neural network unit (identification model) for performing classification (identification) with a neural network (identification) model to generate the evaluation statistics (e.g. accuracy of neural network/identification model)).
Regarding Claim 10,
Shibahara et al. teaches a data processing method that a data processing apparatus which has a processor which executes a program and a storage device which stores the program executes ([0022]: "An example of a data processing apparatus, a data analysis method, and a data analysis program according to a first embodiment will be described hereinafter with reference to the accompanying drawings" teaches a data processing method for a data processing apparatus. Fig. 2; [0033]-[0035]: "FIG. 2 is a block diagram depicting an example of a hardware configuration of the data processing apparatus 100. The data processing apparatus 100 has a processor 201, a storage device 202, an input device 203, an output device 204, a communication interface (communication IF) 205, and an image processing circuit 207 … The processor 201 controls the data processing apparatus 100. The storage device 202 serves as a work area for the processor 201. Furthermore, the storage device 202 is a non-transitory or transitory recording medium storing various programs and data and the object-to-be-analyzed DB … the image processing circuit 207 may be realized by causing the processor 201 to execute programs stored in the storage device 202" teaches the data processing apparatus having a processor and storage device storing a program for execution),
wherein the data processing apparatus is accessible to an analysis target data group which has values of respective variables in a variable group and a value of an objective variable per analysis target and to an element group which defines the variable group and each of one or more modulation method(s) for modulating the variable(s) as elements (Fig 1; Fig. 2; [0033]-[0035]: "FIG. 2 is a block diagram depicting an example of a hardware configuration of the data processing apparatus 100. The data processing apparatus 100 has a processor 201, a storage device 202, an input device 203, an output device 204, a communication interface (communication IF) 205, and an image processing circuit 207 … The processor 201 controls the data processing apparatus 100. The storage device 202 serves as a work area for the processor 201. Furthermore, the storage device 202 is a non-transitory or transitory recording medium storing various programs and data and the object-to-be-analyzed DB … The image processing circuit 207 executes a series of processing (1) to (6) depicted in FIG. 1 while referring to a pattern table 208. The pattern table 208 is stored, for example, in a memory area, not depicted, within the image processing circuit 207. It is noted that while the image processing circuit 207 is realized by the circuit configuration, the image processing circuit 207 may be realized by causing the processor 201 to execute programs stored in the storage device 202" teaches a storage device 202 (storage unit) for storing the object-to-be-analyzed DB 104 (analysis target data group) and a pattern table 208 (e.g. analysis target data group is accessible). Fig. 3; [0036]-[0038]: "FIG. 3 is an explanatory diagram depicting an example of the object-to-be-analyzed DB 104. The object-to-be-analyzed DB 104 has a patient ID 301, an objective variable 302, and a factor group 303 as fields … Furthermore, a modulation method 304 is associated with each factor in the factor group 303. The modulation method 304 is an operator with the value of a factor as an operand" teaches that the object-to-be-analyzed DB 104 (analysis target data group) that has an objective variable 302 (value of an objective variable per analysis target), and a factor group 303 (variable group) which has values of respective factors (variables). Fig. 4; [0039]-[0041]: "FIG. 4 is an explanatory diagram depicting an example of the pattern table 208. The pattern table 208 is a table that specifies the element group 105 used in generating a control signal for formulating the equations 111 and 112 and plotting the coordinate values onto the coordinate space 110 … The pattern table 208 has a control ID 401 and an element number sequence 402 as fields. … The element number sequence 402 is a set of element numbers corresponding to elements selectable by each module identified by the control ID 401 … the elements in the pattern table 208 of FIG. 4 include the types of the factors and the types of the modulation methods" teaches the pattern table 208 specifies an element group 105 with the factors (variable group) and modulation methods for modulating the variables as elements),
the processor executes a modulation process of, when the element which is selected from the element group is acquired, planning a modulation function for modulating a value of the variable which is contained in the acquired element … (Fig. 5; [0042]-[0046]: "FIG. 5 is a block diagram depicting an example of a circuit configuration of the image processing circuit 207. The image processing circuit 207 has a data memory 500, the X-axis modulation unit 510, the Y-axis modulation unit 520, an image generator 530, an evaluator 540, a controller 550, and the pattern table 208. … The X-axis modulation unit 510 configures part of the equation formulation AI 101 depicted in FIG. 1. The X-axis modulation unit 510 sets factors and modulation methods in the X-axis equation 111. … The multiplexer 513 selects a factor x1 from a control signal output from the controller 550. The multiplexer 513 may receive selection of the factor x1 selected by the user … The modulator 515 selects a modulation method opx1 from the control signal output from the controller 550. The modulator 515 applies the modulation method opx1 to all cases related to the factor x1" teaches a modulation unit 510 that plans a modulation method (modulation function) for modulating a value of a selected factor (value of the variable which is contained in the acquired element)) and
modulating the value of the variable per the analysis target on the basis of the modulation function (Fig. 5; [0046]-[0047]: "The modulator 515 selects a modulation method opx1 from the control signal output from the controller 550. The modulator 515 applies the modulation method opx1 to all cases related to the factor x1 … Examples of the modulation method opx1 to be applied include the non-modulation, the sign change, logarithmic transformation (for example, log.sub.10), absolute value transformation, and exponentiation" teaches that the modulation method (modulation function) is applied to the factor (value of the variable per the analysis target) to modulate the factor), and
a generation process of generating image data which gives a point of coordinates which are values of the modulation result and the objective variable to a coordinate space which is defined by a first axis which corresponds to a result of modulation by the modulation process and a second axis which corresponds to the objective variable, per the analysis target (Fig. 5; [0055]-[0056]: "The image generator 530 configures part of the equation formulation AI 101 depicted in FIG. 1. The image generator 530 receives the signals x′ and y′ output from the X-axis modulation unit 510 and the Y-axis modulation unit 520. The signal x′ is a set of x coordinate values (one-dimensional vector) calculated from the X-axis equation 111 per case, while the signal y′ is a set of y coordinate values (one-dimensional vector) calculated from the Y-axis equation 112 per case. The image generator 530 plots the coordinate values at the same locations within the signals x′ and y′ onto the coordinate space 110, thereby rendering pixels that configure the image data I about the coordinate space 110 onto which the patient data is plotted … At that time, the image generator 530 determines a color of each pixel by referring to the objective variable 302 on the data memory 500 … The image generator 530 stores the generated image data I in the data memory 500 and outputs the image data I to the controller 550" teaches an image generator 530 (generation unit) that generates image data which gives coordinate points that are values of modulation outputs (modulation results) and the objective variable to a coordinate space, with axes corresponding to a modulation output (modulation result) from the modulation unit and the objective variable).
Shibahara et al. does not appear to explicitly teach planning a modulation function for modulating a value of the variable which is contained in the acquired element on the basis of an action history which is the history of the acquired element.
However, Kubalik et al. teaches planning a modulation function for modulating a value of the variable which is contained in the acquired element on the basis of an action history which is the history of the acquired element (Fig. 1; Algorithm 1; Section III, D: "In symbolic value iteration (SVI), the optimal value function is found iteratively, just like in standard value iteration [32]. In each iteration l, the value function Vl−1 from the previous iteration is used to compute the target for improving the value function Vl in the current iteration. For each state xi ∈ X, the target ti,l ∈ R is calculated by evaluating the right-hand-side of (5):
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Here, the maximization is carried out over the predefined discrete control action set U. Note that virtually all control systems use discrete control actions … In addition, as the next states and rewards are pre-computed and provided to the SVI algorithm in the data set D (8), we can replace (10) by its computationally more efficient equivalent:
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Given the target ti,l, an improved value function V` is constructed by applying SR [Symbolic Regression] with the following fitness function:
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60
300
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This fitness function is again the mean-squared Bellman error. However, as opposed to (9), the above criterion (12) defines a true regression problem: the target to be fitted is fixed as it is based on Vl−1 from the previous iteration" teaches a symbolic value iteration wherein the modulations for the value function for the target (selected) element are planned based on the set of discrete control actions (action history) over time (e.g. the mathematical symbol modulation planned for the value function is based on the discrete control actions)).
Shibahara et al. and Kubalik et al. are analogous to the claimed invention because they are directed towards machine learning data processing using reinforcement learning.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate planning a modulation function for modulating a value of the variable which is contained in the acquired element on the basis of an action history which is the history of the acquired element as taught by Kubalik et al. to the claimed invention of Shibahara et al.
One of ordinary skill in the art would have been motivated to make this modification to produce "value functions [that] yield well-performing policies and are compact, mathematically tractable, and easy to plug into other algorithms" (Kubalik et al. Abstract).
Regarding Claim 11,
Shibahara et al. teaches a data processing program ([0022]: "An example of a data processing apparatus, a data analysis method, and a data analysis program according to a first embodiment will be described hereinafter with reference to the accompanying drawings" teaches a data processing program for a data processing apparatus. Fig. 2; [0033]-[0035]: "FIG. 2 is a block diagram depicting an example of a hardware configuration of the data processing apparatus 100. The data processing apparatus 100 has a processor 201, a storage device 202, an input device 203, an output device 204, a communication interface (communication IF) 205, and an image processing circuit 207 … The processor 201 controls the data processing apparatus 100. The storage device 202 serves as a work area for the processor 201. Furthermore, the storage device 202 is a non-transitory or transitory recording medium storing various programs and data and the object-to-be-analyzed DB … the image processing circuit 207 may be realized by causing the processor 201 to execute programs stored in the storage device 202" teaches the data processing apparatus having a processor and storage device storing a program for execution) for
making a processor which is accessible to an analysis target data group which has values of respective variables in a variable group and a value of an objective variable per analysis target and to an element group which defines the variable group and each of one or more modulation method(s) of modulating the variable(s) as elements (Fig 1; Fig. 2; [0033]-[0035]: "FIG. 2 is a block diagram depicting an example of a hardware configuration of the data processing apparatus 100. The data processing apparatus 100 has a processor 201, a storage device 202, an input device 203, an output device 204, a communication interface (communication IF) 205, and an image processing circuit 207 … The processor 201 controls the data processing apparatus 100. The storage device 202 serves as a work area for the processor 201. Furthermore, the storage device 202 is a non-transitory or transitory recording medium storing various programs and data and the object-to-be-analyzed DB … The image processing circuit 207 executes a series of processing (1) to (6) depicted in FIG. 1 while referring to a pattern table 208. The pattern table 208 is stored, for example, in a memory area, not depicted, within the image processing circuit 207. It is noted that while the image processing circuit 207 is realized by the circuit configuration, the image processing circuit 207 may be realized by causing the processor 201 to execute programs stored in the storage device 202" teaches a storage device 202 (storage unit) for storing the object-to-be-analyzed DB 104 (analysis target data group) and a pattern table 208 (e.g. analysis target data group is accessible). Fig. 3; [0036]-[0038]: "FIG. 3 is an explanatory diagram depicting an example of the object-to-be-analyzed DB 104. The object-to-be-analyzed DB 104 has a patient ID 301, an objective variable 302, and a factor group 303 as fields … Furthermore, a modulation method 304 is associated with each factor in the factor group 303. The modulation method 304 is an operator with the value of a factor as an operand" teaches that the object-to-be-analyzed DB 104 (analysis target data group) that has an objective variable 302 (value of an objective variable per analysis target), and a factor group 303 (variable group) which has values of respective factors (variables). Fig. 4; [0039]-[0041]: "FIG. 4 is an explanatory diagram depicting an example of the pattern table 208. The pattern table 208 is a table that specifies the element group 105 used in generating a control signal for formulating the equations 111 and 112 and plotting the coordinate values onto the coordinate space 110 … The pattern table 208 has a control ID 401 and an element number sequence 402 as fields. … The element number sequence 402 is a set of element numbers corresponding to elements selectable by each module identified by the control ID 401 … the elements in the pattern table 208 of FIG. 4 include the types of the factors and the types of the modulation methods" teaches the pattern table 208 specifies an element group 105 with the factors (variable group) and modulation methods for modulating the variables as elements) execute
a modulation process of, when the element which is selected from the element group is acquired, planning a modulation function for modulating a value of the variable which is contained in the acquired element … (Fig. 5; [0042]-[0046]: "FIG. 5 is a block diagram depicting an example of a circuit configuration of the image processing circuit 207. The image processing circuit 207 has a data memory 500, the X-axis modulation unit 510, the Y-axis modulation unit 520, an image generator 530, an evaluator 540, a controller 550, and the pattern table 208. … The X-axis modulation unit 510 configures part of the equation formulation AI 101 depicted in FIG. 1. The X-axis modulation unit 510 sets factors and modulation methods in the X-axis equation 111. … The multiplexer 513 selects a factor x1 from a control signal output from the controller 550. The multiplexer 513 may receive selection of the factor x1 selected by the user … The modulator 515 selects a modulation method opx1 from the control signal output from the controller 550. The modulator 515 applies the modulation method opx1 to all cases related to the factor x1" teaches a modulation unit 510 that plans a modulation method (modulation function) for modulating a value of a selected factor (value of the variable which is contained in the acquired element)) and
modulating the value of the variable per the analysis target on the basis of the modulation function (Fig. 5; [0046]-[0047]: "The modulator 515 selects a modulation method opx1 from the control signal output from the controller 550. The modulator 515 applies the modulation method opx1 to all cases related to the factor x1 … Examples of the modulation method opx1 to be applied include the non-modulation, the sign change, logarithmic transformation (for example, log.sub.10), absolute value transformation, and exponentiation" teaches that the modulation method (modulation function) is applied to the factor (value of the variable per the analysis target) to modulate the factor), and
a generation process of generating image data which gives a point of coordinates which are values of the modulation result and the objective variable to a coordinate space which is defined by a first axis which corresponds to a result of modulation by the modulation process and a second axis which corresponds to the objective variable, per the analysis target (Fig. 5; [0055]-[0056]: "The image generator 530 configures part of the equation formulation AI 101 depicted in FIG. 1. The image generator 530 receives the signals x′ and y′ output from the X-axis modulation unit 510 and the Y-axis modulation unit 520. The signal x′ is a set of x coordinate values (one-dimensional vector) calculated from the X-axis equation 111 per case, while the signal y′ is a set of y coordinate values (one-dimensional vector) calculated from the Y-axis equation 112 per case. The image generator 530 plots the coordinate values at the same locations within the signals x′ and y′ onto the coordinate space 110, thereby rendering pixels that configure the image data I about the coordinate space 110 onto which the patient data is plotted … At that time, the image generator 530 determines a color of each pixel by referring to the objective variable 302 on the data memory 500 … The image generator 530 stores the generated image data I in the data memory 500 and outputs the image data I to the controller 550" teaches an image generator 530 (generation unit) that generates image data which gives coordinate points that are values of modulation outputs (modulation results) and the objective variable to a coordinate space, with axes corresponding to a modulation output (modulation result) from the modulation unit and the objective variable).
Shibahara et al. does not appear to explicitly teach planning a modulation function for modulating a value of the variable which is contained in the acquired element on the basis of an action history which is the history of the acquired element.
However, Kubalik et al. teaches planning a modulation function for modulating a value of the variable which is contained in the acquired element on the basis of an action history which is the history of the acquired element (Fig. 1; Algorithm 1; Section III, D: "In symbolic value iteration (SVI), the optimal value function is found iteratively, just like in standard value iteration [32]. In each iteration l, the value function Vl−1 from the previous iteration is used to compute the target for improving the value function Vl in the current iteration. For each state xi ∈ X, the target ti,l ∈ R is calculated by evaluating the right-hand-side of (5):
PNG
media_image1.png
38
332
media_image1.png
Greyscale
Here, the maximization is carried out over the predefined discrete control action set U. Note that virtually all control systems use discrete control actions … In addition, as the next states and rewards are pre-computed and provided to the SVI algorithm in the data set D (8), we can replace (10) by its computationally more efficient equivalent:
PNG
media_image2.png
32
280
media_image2.png
Greyscale
Given the target ti,l, an improved value function V` is constructed by applying SR [Symbolic Regression] with the following fitness function:
PNG
media_image3.png
60
300
media_image3.png
Greyscale
This fitness function is again the mean-squared Bellman error. However, as opposed to (9), the above criterion (12) defines a true regression problem: the target to be fitted is fixed as it is based on Vl−1 from the previous iteration" teaches a symbolic value iteration wherein the modulations for the value function for the target (selected) element are planned based on the set of discrete control actions (action history) over time (e.g. the mathematical symbol modulation planned for the value function is based on the discrete control actions)).
Shibahara et al. and Kubalik et al. are analogous to the claimed invention because they are directed towards machine learning data processing using reinforcement learning.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate planning a modulation function for modulating a value of the variable which is contained in the acquired element on the basis of an action history which is the history of the acquired element as taught by Kubalik et al. to the claimed invention of Shibahara et al.
One of ordinary skill in the art would have been motivated to make this modification to produce "value functions [that] yield well-performing policies and are compact, mathematically tractable, and easy to plug into other algorithms" (Kubalik et al. Abstract).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to BRIAN J HALES whose telephone number is (571)272-0878. The examiner can normally be reached M-F 9:00am - 5:00pm.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Kamran Afshar can be reached at (571) 272-7796. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/BRIAN J HALES/Examiner, Art Unit 2125
/KAMRAN AFSHAR/Supervisory Patent Examiner, Art Unit 2125