DETAILED ACTION
Status of Claims
Claim(s) 1-3 are pending and are examined herein.
Claim(s) 1-3 are rejected under 35 U.S.C. § 112(b) and 101.
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION. —The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claim(s) 1-3 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor, for pre-AIA the applicant regards as the invention.
Regarding Claim 1,
XZZzzZtteachclaim 1 is rejected as being indefinite for failing to particularly point out and distinctly claim the subject matter regarded as the invention.
The claim is rejection as being indefinite for the following reasons:
The claim recites “in which division characteristics and eigenvalues of left and right subtrees of nodes on each layer of the tree are randomly selected, data of an identification target and data of other persons are randomly selected as a training set for pre-training the model, for an identification application, identifying users successfully means identifying self data as normal and other persons' data as abnormal, that is, an output resulted from inputting the self data into the model is True, and an output resulted from inputting the other persons' data into the model is False, thus a problem of identifying whether the current user is self is transformed into a two-category problem, so that the self data and other persons' data are distinguished; meanwhile each of the users has his own identification model established, in which non-self data will be identified as abnormal, thus the tree model is used as a basic model for identification;” lines 6-16.
In Step 2 of claim 1, the claim recites a descriptive passage beginning with “in which division characteristics and eigenvalues...” and continuing with transitional and definition phrases such as “identifying users successfully means...”, “that is...”, “thus a problem... is transformed...”, “so that...”, “meanwhile each of the user...”, and “thus the tree model is used as a basic model for identification.” These recitations are drafted in a descriptive, explanatory format rather than reciting clear and definite method acts. The claim does not clearly specify which portions of this paragraph constitute actual limitations that must be performed to implement the method and which portions represent non-limiting background description or intended results.
The recited descriptive paragraph introduces terms such as “the model,” “the tree model,” the current user,” and “each of the users” without clear antecedent basis. It further mixes steps with descriptive statements (e.g., “successfully means,” “that is,” “thus... is transformed,” “so that”), creating uncertainty as to which actions are required limitations versus narrative description. Additionally, phrases such as “meanwhile” and “thus the tree model” lack clarity regarding when these events occur, what performs them, and whether they are required steps by the method.
Because the recited paragraph is drafted in a descriptive form rather than as clear method steps/limitations, it is unclear whether the recited language imposes actual operational requirements on the claimed method or merely provides description that is not required to be implemented by the claimed method.
The claim further recites: “Step 2: .... in the tree model, firstly a depth of the tree is determined, and characteristic dimensions and eigenvalues used to divide each of the nodes are randomly selected when the model is trained, each data traverses a whole structure of the tree model and is classified into left or right subtrees according to characteristic dimensions and eigenvalues of the nodes, if the eigenvalues of the data are smaller than that of the nodes, the data will be classified into the left subtree, and if the eigenvalues of the data are larger than or equal to that of the nodes, the data will be classified into the right subtree, and so on, until the data falls on a certain leaf node, and traversing of the data ends, a preliminary training model is obtained after all of the training data have traversed; data of the same person will fall on a same node with a large probability, since the self data is more than the other persons' data, a sample density in the node where the self data is located is higher than that in other nodes, then the abnormal scores of each data are calculated for the sample density in each node according to Formula (1) - (3), the higher the score, the more likely the data is abnormal data, namely, non-self data; in order to avoid mistakes caused by contingency, the identification model established for the users is consist of plural different tree models, the data is input into each of the tree models to obtain abnormal scores of each tree, then final abnormal scores are obtained in average, the data is classified into two categories according to a relativity of the scores to a classification threshold: normal or abnormal, if the abnormal score is above the threshold, the data is abnormal, and if the abnormal score is below the threshold, the data is normal, thus distinguishing self from non-self; a calculation process of the abnormal scores is as follows, assuming that a certain sample data falls on a leaf node of the i-th tree, a density of the leaf node is:” lines 17-39.
The recited extended descriptive paragraph introduces multiple issues that render the claim indefinite. In particular, the paragraph is drafted in explanatory format rather than as a definite sequence of method steps, and contains several contingent, hypothetical, improper antecedent basis, and descriptive statements that fail to impose clear limitations on the claimed process. The following identified issues would prevent a person ordinary skill in the art from determining the metes and bounds of the claim with reasonable certainty:
The recited paragraph uses descriptive transitions such as “firstly,” “thus,” “that is,” “meanwhile,” “so that,” and “so on” that provide explanation rather than reciting definite steps. It is not clear what actions the method actually requires versus what constitute background explanation leaving the scope uncertain.
The claim contains multiple contingent statements such as “if the eigenvalues of the data are smaller than .... if the eigenvalues of the data are larger than or equal ... if the abnormal score is above the threshold ..,etc.” Under MPEP § 2111.04, the broadest reasonable interpretation of a method claim includes only those steps that must be performed, and does not include steps that are optional or dependent on conditions that may not occur. Here, the claim does not clearly define which conditional statements/events are required by the claimed method (e.g., musth the eigenvalue comparison be performed for every node traversal, or only when a particular condition is met?), or which branch(es) of the conditional statement must be executed.
The phrase “assuming that a certain sample data falls on a leaf node...” introduces a hypothetical scenario rather than a step required by the method. It is not clear whether this condition must occur for the method to be practiced, or whether the remainder of the paragraph including the mathematical formulas and relationships (lines 40-55) applies only under this unverified assumption.
The statements such as “data of the same person will fall on a same node with a large probability,” “thus distinguishing self from non-self,” “namely, non-self data” and “in order to avoid mistakes caused by contingency” fail to provide the specific action being performed by the method making the claimed statements non-limiting when they merely describe the intended results. Thus, it is unclear whether these statements are intended to provide a specific action to be performed, describe the intended results of the claim step, or merely provide descriptive context of the identification model. Under MPEP § 2111.04(I), clauses are not limiting when they merely articulate intended results. Because the claim does not specify whether these limitations impose actual requirements or merely provide explanatory context, it is unclear what weight these statements have in defining the scope of the method.
The claim paragraph recites subjective or relative terms such as “larger probability,” “higher density,” and “relativity of the scores.” Because no objective thresholds or criteria are provided, it is not clear what degree or range of values is required, making boundaries of the claimed undefined. For example, the claim states “a sample density in the node where the self data is located is higher than that in other nodes ...” that is not an operation step but an observational description of expected behavior of the tree structure. It is unclear to determine when “density” is sufficiently “higher”, whether any difference is enough, and/or whether the claim step requires computing density.
The claim introduces multiple terms without proper antecedent basis, including “the tree model,” “the node,” “each tree,” “classification threshold (lines 33, 47, and 49),” “other persons’ data,” “abnormal score(s).” These terms are not clearly tied to previously introduced elements, which provides improver antecedent basis issue that renders the claim indefinite. For example, the claim used inconsistently different terms such as: the abnormal scores, abnormal score, final abnormal scores, an abnormal score, an overall abnormal scores, and a calculated abnormal score, without proper antecedent basis in these terms.
The claim recites: “assuming that the identification model is consist of" M trees", then an overall abnormal score y of the sample data X is:” where the use of “assuming” introduces a hypothetical condition rather than actual step being applied. The phrase “the identification model” lacks antecedent basis and is unclear as to whether it refers to initially constructed tree model, a pre-trained model, or different identification model. The term “the sample X” is introduced without any prior antecedent basis. It is unclear what data x represents (e.g., self data, non-self data, training data, or new sample), making the scope of the claimed step undefined.
The claim recites the terms “the data of the identification target and the data of the other persons” without a clear antecedent basis for these terms. There is no prior introduction of “a data of the identification target” or “a data of the other persons.”
The claim step further recites: “wherein, is the number of samples whose history falls on the node, and is the number of layer in the tree where the node is located, then an abnormal score of the i-th tree is:” where the claim fail to define the variables in the claimed formula (1) (i.e.,
m
i
,
v
i
,
h
i
)
and it is not clear what the claim suggest by the recitations of “is the number of samples whose history falls on the node” and “is the number of layer in the tree where the node is located.”, and which variables corresponds to which meaning. As written, the claim omits the variables names and therefore fails to particularly point out and distinctly claim the subject matter of the invention.
The recitation of: “when a new sample data is classified with the identification model, if a calculated abnormal score is smaller than the classification threshold, the associated user will be identified as self, otherwise identified as non-self;” renders the claim indefinite. The recited term “the associated user” lacks antecedent basis and the claim does not specify how the sample data is associated with any user prior to performing the classification. Moreover, the recited conditional statement (“if... otherwise...”) introduces a contingent limitation without clearly specifying whether the claim process requires the condition or whether both outcomes are required steps of the claimed method.
Accordingly, Step 2 of the claim 1 fails to particularly point out and distinctly claim the subject matter which inventor regards as the invention, and one of ordinary skill in the art cannot determined the scope of the claimed method with reasonable certainty.
The claim further recites: “Step 3: performing identification with the initial identification model, and sending the identification result to the expert for judgment at a random probability for each identification, in which the expert judges whether the identification result is correct, if the identification result is correct, then the expert feedback is positive, and if the identification result is incorrect, then the expert feedback is negative;” lines 51-55.
The terms “the identification results” and “the expert” lack antecedent basis in the claim. The claim does not introduce these term and it is unclear what outcomes is evaluated and who qualifies as the expert. Further, the claim does not define what constitutes the “random probability” or how it is determined. It is unclear whether this means fixed percentage, or probabilistic function. Thus, one of ordinary skill in the art cannot determine when and how often an identification must be sent to the expert. Moreover, the claim define conditional statements of the expected behavior of a human expert reviewing the identification results. These contingent statements are part of the expert analysis and not actual step of the process. Further, the claim doesn’t clearly constrain the process to require this feedback every identification. See MPEP § 2111.04.
Accordingly, one of ordinary skill in the art cannot determine the scope of Step 3 with reasonable certainty.
The claim further recites: “Step 4: adjusting and updating the identification model according to the expert feedback in four ways including increasing the node density
m
i
, decreasing the node density
m
i
,downward growing the tree, and upward incorporating the sub-tree; for the leaf node where the data falls after traversing the tree structure, constructing a local node likelihood to measure rationality of the current tree structure, the local node likelihood being defined as: ... t indicates an identification result, there are only two results,
t
=
1
(abnormal, non-self) and
t
=
0
(normal, self); ... then determining a final adjustment strategy according to whether the value of
r
i
and
g
i
are positive or negative, in which a. If both
r
i
and
g
i
are positive, it is proved that
m
i
should be increased to make the joint function more optimal, if a brother node of the leaf node has no historical negative feedback, then the left and right nodes combined upward, if the brother node of the leaf node has historical negative feedback, then the node density
m
i
is increased; b. If both
r
i
and
g
i
are negative, it is proved that
m
i
should be decreased to make the joint function more optimal, if a depth of the current tree model has not reached a maximum depth, then the tree is downward grown so that the abnormal data will be more dispersed, if the depth of the current tree model has reached the maximum depth and the tree cannot be grown downward, then the node density
m
i
is decreased; c. If one of
r
i
and
g
i
is positive and the other of them is negative, it is necessary to grow the tree downward, through setting a characteristic dimension and eigenvalue for node division, normal and abnormal samples are classified into left and right sub-nodes, so as to be classified into different nodes;” lines 56-94.
Step 4 recites the term “rationality” in the claim limitation “constructing a local node likelihood to measure rationality of the current tree structure,” which is a relative term that renders the claim indefinite. The claim does not define the term or the objective of what constitutes rational. Step 4 further recites the term “an identification results” where it is unclear whether the claim introduce a new identification results or refers back to the previously recited identification results in Step 3 that lacks antecedent basis in the claim. Additionally, the conditional statements (a-c) recited in Step 4 represent contingent limitations under MPEP § 2111.04, but the claim fails to specify whether all conditions are required to be performed by the method claim during each execution of step 4 or only when certain situation occurs. Thus, it is unclear whether the method can be practiced without performing one or more of these conditions.
Accordingly, one of ordinary skill in the art cannot determine the metes and bounds of Step 4 with reasonable certainty, rendering the claim indefinite under 35 U.S.C. § 112(b).
The claim further recites: “Step 5: performing the adjustment process in step 4 each time when the feedback data is generated, and continuing a next identification with the adjusted and updated identification model, then repeating step 3 and step 4 until the model reaching a required accuracy.” lines 95-97.
Step 5 recites terms “the feedback data” and “the model” without a clear antecedent basis in the claim. The term feedback data was not defined in step 4; thus, it is unclear whether the claim step is referring to the expert feedback that was further lacks sufficient antecedent basis in the claim or to different data from the model. Additionally, the claim fails to provide consistent antecedent basis for “the model.” Further, the claim recites “a required accuracy” where the claim should recite “a predefined accuracy.”
As outlined above, Claim 1 fail to particularly point out and distinctly claim the subject matter. The claim recites multiple descriptive statements in a paragraph form, making it unclear which elements are active functional limitations versus explanatory description. Accordingly, one of ordinary skill in the art would not be able to determine, with reasonable certainty, the scope of the claimed method.
Regarding Claim 2,
XZZzzZtteachdependent claim 2 recites similar issues identified above and inherits the deficiencies of the respective parent claim 1.
Regarding Claim 3, dependent claim 3
XZZzzZtteachinherits the deficiencies of the respective parent claim 1. The claim further recites “In the step 3, the current identification result is given to the expert for feedback with a probability of 20%.” The terms “the current identification result” and “the expert” lack clear antecedent basis in the respective claim 1, making it unclear which results is being evaluated and which expert is being referred to.
In view of the above, Examiner respectfully requests that Applicant thoroughly review all claims for compliance with the requirements set forth under 35 U.S.C. § 112. Appropriate correction is required.
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.
When considering subject matter eligibility under 35 U.S.C. 101, it must be determined whether the claim is directed to one of the four statutory categories of invention, i.e., process, machine, manufacture, or composition of matter (Step 1). If the claim does fall within one of the statutory categories, the second step in the analysis is to determine whether the claim is directed to a judicial exception (Step 2A). The Step 2A analysis is broken into two prongs. In the first prong (Step 2A, Prong 1), it is determined whether or not the claims recite a judicial exception (e.g., mathematical concepts, mental processes, certain methods of organizing human activity). If it is determined in Step 2A, Prong 1 that the claims recite a judicial exception, the analysis proceeds to the second prong (Step 2A, Prong 2), where it is determined whether or not the claims integrate the judicial exception into a practical application. If it is determined at step 2A, Prong 2 that the claims do not integrate the judicial exception into a practical application, the analysis proceeds to determining whether the claim is a patent-eligible application of the exception (Step 2B). If an abstract idea is present in the claim, any element or combination of elements in the claim must be sufficient to ensure that the claim integrates the judicial exception into a practical application, or else amounts to significantly more than the abstract idea itself. Applicant is advised to consult MPEP 2106 for more details of the analysis.
Under Step 1 analysis,
Claims 1-3 recite an identification method (representing a process).
Therefore, the claims falls into one of the four statutory categories (i.e., process, machine, article of manufacture, or composition of matter).
Claims
1-3 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more, and hence is not patent-eligible subject matter.
Regarding Claim 1,
Step 2A Prong 1: The claim recites an abstract idea enumerated in the 2019 PEG.
performing characteristic extraction on the acquired perceptual data, and distinguishing different persons with the extracted characteristic, with an accuracy of more than 70% using random forest algorithm, with feasibility for identification; (An abstract idea of mental process and mathematical concepts. Examiner’s note: Under the broadest reasonable interpretation, this limitation merely describe a data-preprocessing step in which collected information is analyzed to extract content and distinguish different persons’ data using a statistical/mathematical algorithm (e.g., a random forest). Such operations represents abstract ideas, including (i) data analysis and feature extraction that can be performed in the human mentally with the aid of pen and paper, and (ii) the use of mathematical/statistical algorithm. These fall within the mental processes and mathematical concepts grouping of abstract ideas. See MPEP § 2106.04(a)(2)(I) & (III).)
Step 2: constructing an initial identification model that is based on a tree structure, in which division characteristics and eigenvalues of left and right subtrees of nodes on each layer of the tree are randomly selected, data of an identification target and data of other persons are randomly selected as a training set for pre-training the model, for an identification application identifying users successfully means identifying self data as normal and other persons' data as abnormal, that is, an output resulted from inputting the self data into the model is True, and an output resulted from inputting the other persons' data into the model is False, thus a problem of identifying whether the current user is self is transformed into a two-category problem, so that the self data and other persons' data are distinguished; meanwhile each of the users has his own identification model established, in which non-self data will be identified as abnormal, thus the tree model is used as a basic model for identification; in the tree model, firstly a depth of the tree is determined, and characteristic dimensions and eigenvalues used to divide each of the nodes are randomly selected when the model is trained, each data traverses a whole structure of the tree model and is classified into left or right subtrees according to characteristic dimensions and eigenvalues of the nodes, if the eigenvalues of the data are smaller than that of the nodes, the data will be classified into the left subtree, and if the eigenvalues of the data are larger than or equal to that of the nodes, the data will be classified into the right subtree, and so on, until the data falls on a certain leaf node, and traversing of the data ends, a preliminary training model is obtained after all of the training data have traversed; data of the same person will fall on a same node with a large probability, since the self data is more than the other persons' data, a sample density in the node where the self data is located is higher than that in other nodes, then the abnormal scores of each data are calculated for the sample density in each node according to Formula (1) - (3), the higher the score, the more likely the data is abnormal data, namely, non-self data; in order to avoid mistakes caused by contingency, the identification model established for the users is consist of plural different tree models, the data is input into each of the tree models to obtain abnormal scores of each tree, then final abnormal scores are obtained in average, the data is classified into two categories according to a relativity of the scores to a classification threshold: normal or abnormal, if the abnormal score is above the threshold, the data is abnormal, and if the abnormal score is below the threshold, the data is normal, thus distinguishing self from non-self; a calculation process of the abnormal scores is as follows, assuming that a certain sample data falls on a leaf node of the i-th tree, a density of the leaf node is:
m
i
=
v
i
×
2
h
i
(1) wherein, is the number of samples whose history falls on the node, and is the number of layer in the tree where the node is located, then an abnormal score of the i-th tree is:
y
i
=
1
-
S
i
m
i
,
(2) wherein,
S
i
m
i
is a cumulative distribution function of logistic distribution:
s
i
m
i
;
μ
i
,
σ
i
=
1
1
+
e
x
p
3
(
μ
i
-
m
i
)
π
σ
i
, (3) wherein,
μ
i
and
σ
i
respectively indicates an expected value and standard deviation of the node density
m
i
in eigenspace; assuming that the identification model is consist of" M trees", then an overall abnormal score y of the sample data X is:
y
=
1
M
∑
i
=
0
M
y
i
(4)
the data of the identification target and the data of the other persons are randomly selected as the training set for model pre-training, the abnormal scores of training of the sample data are ranked in a descending order, and a classification threshold is selected, when a new sample data is classified with the identification model, if a calculated abnormal score is smaller than the classification threshold, the associated user will be identified as self, otherwise identified as non-self; (An abstract idea of “a Mental Step” and/or “Mathematical Concept.” The “Constructing” step, as drafted, and under its broadest reasonable interpretation, covers concepts that would fall under the mental process and mathematical concept grouping of abstract idea. Examiner note: Under its broadest reasonable interpretation, Step 2 describes a series of mental processes and mathematical concepts performed on the data. Step 2 describes conceptual operations including (i) selecting data, constructing tree with nodes (ii) deciding whether data belongs to left or right nodes, (iii) identifying whether data is self or non-self, (iv) calculating densities and abnormal scores using formulas, (v) ranking scores, and (vi) comparing and determining a classification score based on a threshold. These recitations fall within the judicial exception of abstract idea groupings. Step 2 describes operations that would fall under the abstract idea grouping of mental processes, such as observations, evaluation, and decision-making. For example, selecting training data, constructing a tree model using nodes and connecting values, determining which subtree data should fall into, determining and ranking abnormal scores, and deciding whether a score is above or below a threshold constitute mental steps that can be practically performed in the human mind with the aid of pen and paper. The steps also defines the identification model using mathematical relationships, formulas, and calculations. These mathematical operations include computing sample densities, computing abnormal scores, applying a logistic cumulative distribution function, and averaging scores across multiple trees. These operations are defined and expressed using mathematical formulas and relationships that can be performed manually with the aid of pen and paper. Step 2 merely provides a descriptive format of constructing an identification model based on mathematical relationships, formulas, calculations, mental evaluations, and decision-making processes to perform the claimed step, rather than defining a technical implementation of the model construction. Accordingly, Step 2 is directed to the abstract idea of (i) mental processes and (ii) mathematical concepts. See MPEP § 2106.04(a)(2)(I) & (III).)
Step 3: performing identification with the initial identification model, ... in which the expert judges whether the identification result is correct, if the identification result is correct, then the expert feedback is positive, and if the identification result is incorrect, then the expert feedback is negative; (An abstract idea of “a Mental Step” and/or “Mathematical Concept.” The “identification results” step, as drafted, and under its broadest reasonable interpretation, covers concepts that falls under the mental process and mathematical concepts. Examiner note: this step merely applies the mathematical relationships, formulas, and mental processes described in Step 2 to determine whether the data is self/non-self or normal/abnormal. The claim further recites a human expert judging whether the determined results is correct or incorrect, and providing positive or negative feedback. These operations, evaluating identification results, making determinations, and providing feedback, represent mental processes that can be practically performed in the human mind. Accordingly, Step 3 is directed to the abstract idea of (i) mental processes and (ii) mathematical concepts. See MPEP § 2106.04(a)(2)(I) & (III).)
Step 4: adjusting and updating the identification model according to the expert feedback in four ways including increasing the node density
m
i
, decreasing the node density
m
i
,downward growing the tree, and upward incorporating the sub-tree; for the leaf node where the data falls after traversing the tree structure, constructing a local node likelihood to measure rationality of the current tree structure, the local node likelihood being defined as:
L
i
k
e
l
i
h
o
o
d
r
=
∏
j
=
1
a
i
P
t
j
=
1
;
m
i
∏
l
=
1
n
i
P
(
t
l
=
0
;
m
i
)
(5)
and a current sample likelihood being defined as
L
i
k
e
l
i
h
o
o
d
x
=
y
t
1
-
y
1
-
t
(6)
Wherein
L
i
k
e
l
i
h
o
o
d
r
and
L
i
k
e