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 .
DETAILED ACTION
The instant application having Application No. 18147262 has a total of 25 claims pending in the application.
Information Disclosure Statement
As required by M.P.E.P 609(c), the applicant’s submissions of the Information Disclosure Statement dated 5/4/2026 is acknowledged by the examiner and the cited references have been considered in the examination of the claims now pending. As required by M.P.E.P 609 C(2), a copy of the PTOL-1449 initialed and dated by the examiner is attached to the instant office action.
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/apply/applying-online/eterminal-disclaimer.
Claims 1-25 are provisionally rejected on the ground of nonstatutory double patenting as being unpatentable over claim 1 of copending Application No. 18328185. Although the claims at issue are not identical, they are not patentably distinct from each other because each limitation of the independent claims can be met as seen below.
Instant Application
18328185
Examiners note
A probabilistic classification system comprising
Claim 1: an Anomaly detection system comprising:
This denotes the classification being an anomaly
A memory
Claim 1: a memory
One or more processors
Claim 1: one or more processors
Logic operable to cause the one or more processors to:
Claim 1: one or more processors configured to cause
Obtain, in association with a learning process, a plurality of input vectors
Claim 1: obtaining, in association with a learning process, a plurality of input vectors
Iteratively process the input vectors to compute a knowledge map
Claim 1: iteratively processing the input vectors to compute a knowledge map
Iteratively process the input vectors to determine data associated with one or more knowledge elements
Claim 1: iteratively processing the input vectors to determine metadata associated with one or more knowledge elements
Determine whether an input vector is within a knowledge element (KE) based on the knowledge map and the data
Claim 1: determining an anomaly value based on the knowledge map and the metadata
The use of this to classify anomaly is just intended use.
When it is determined that the input vector is not within a KE, identify one or more neighboring KEs in a vicinity of the input vector
Claim 1: raising an alert that an anomaly was detected if the anomaly value traverses a threshold
EN: It would be obvious to one of ordinary skill in the art at the time of filing to make use of whatever Knowledge Elements were near the incoming data in order to determine which one they match and alert to an anomaly if the match was close enough to be of interest to the system, in order to protect the system from attacks or other dangerous data even when the match is not exact.
Determine a probabilistic classification based on the identification of the one or more neighboring KEs
Claim 3: identifying an anomaly event based on a statistical measure…
This is a provisional nonstatutory double patenting rejection because the patentably indistinct claims have not in fact been patented.
As per claims 2-25, these claims are rejected for similar reasons to 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.
Claims 1-25 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1: Claim 1 is a machine claim. Claim 9 is a manufacture claim and claim 17 is a process claim. Therefore, claims 1-24 are directed to either a process, machine, manufacture or composition of matter.
As per claim 1,
2A Prong 1:
“iteratively process the input vectors to compute a knowledge map” A user mentally or with pencil and paper looks at data and connects it together into a knowledge map.
“iteratively process the input vectors to determine metadata associated with one or more knowledge elements” The user mentally or with pencil and paper determines metadata about the knowledge elements of the knowledge graph.
“determine whether an input vector is within a knowledge element (KE) based on the knowledge map and the metadata” The user mentally or with pencil and paper determines whether input data matches the elements of the knowledge map.
“When it is determined that the input vector is not within a KE, identify one or more neighboring KEs in a vicinity of the input vector” The user mentally or with pencil and paper looks to see if there are any neighboring KEs.
“Determine a probabilistic classification based on the identification of the one or more neighboring KEs” The user mentally or with pencil and paper determines the class based upon nearby KEs.
2A Prong 2: This judicial exception is not integrated into a practical application.
Additional elements:
Memory, one or more processors (mere instructions to apply the exception using a generic computer component);
A learning process (Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f) – Examiner’s note: “learning process” is a generic machine learning algorithm with no particular limitations or details that make it anything more than an off the shelf, generic machine learning process.
“obtain …, a plurality of input vectors” (Adding insignificant extra-solution activity to the judicial exception - see MPEP 2106.05(g)).
2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Additional elements:
Memory, one or more processors (mere instructions to apply the exception using a generic computer component)
A learning process (Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f) – Examiner’s note: “learning process” is a generic machine learning algorithm with no particular limitations or details that make it anything more than an off the shelf, generic machine learning process.
“obtain …, a plurality of input vectors” (MPEP 2106.05(d)(II) indicate that merely “receiving and transmitting data” is a well‐understood, routine, conventional function when it is claimed in a merely generic manner (as it is in the present claim). Thereby, a conclusion that the claimed obtaining step is well-understood, routine, conventional activity is supported under Berkheimer).
As per claims 2, 4, and 7, these claims contain similar mental steps to claim 1 and are rejected for similar reasons to claim 1.
As per claim 3, 5-6, 8 and 25, these claims contain similar mental steps and generic hardware to claim 1 and are rejected for similar reasons to claim 1.
As per claim 9,
2A Prong 1:
“iteratively processing the input vectors to compute a knowledge map” A user mentally or with pencil and paper looks at data and connects it together into a knowledge map.
“iteratively processing the input vectors to determine metadata associated with one or more knowledge elements” The user mentally or with pencil and paper determines metadata about the knowledge elements of the knowledge graph.
“determining whether an input vector is within a knowledge element (KE) based on the knowledge map and the metadata” The user determines whether input data matches the elements of the knowledge map.
“When it is determined that the input vector is not within a KE, identify one or more neighboring KEs in a vicinity of the input vector” The user mentally or with pencil and paper looks to see if there are any neighboring KEs.
“Determining a probabilistic classification based on the identification of the one or more neighboring KEs” The user mentally or with pencil and paper determines the class based upon nearby KEs.
2A Prong 2: This judicial exception is not integrated into a practical application.
Additional elements:
A non-transitory computer readable medium, one or more processors (mere instructions to apply the exception using a generic computer component);
A learning process (Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f) – Examiner’s note: “learning process” is a generic machine learning algorithm with no particular limitations or details that make it anything more than an off the shelf, generic machine learning process.
“obtaining …, a plurality of input vectors” (Adding insignificant extra-solution activity to the judicial exception - see MPEP 2106.05(g)).
2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Additional elements:
A non-transitory computer readable medium, one or more processors (mere instructions to apply the exception using a generic computer component)
A learning process (Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f) – Examiner’s note: “learning process” is a generic machine learning algorithm with no particular limitations or details that make it anything more than an off the shelf, generic machine learning process.
“obtaining …, a plurality of input vectors” (MPEP 2106.05(d)(II) indicate that merely “receiving and transmitting data” is a well‐understood, routine, conventional function when it is claimed in a merely generic manner (as it is in the present claim). Thereby, a conclusion that the claimed obtaining step is well-understood, routine, conventional activity is supported under Berkheimer).
As per claims 10, 12, and 15, these claims contain similar mental steps to claim 9 and are rejected for similar reasons to claim 9.
As per claim 11, 13-14, and 16, these claims contain similar mental steps and generic hardware to claim 9 and are rejected for similar reasons to claim 9.
As per claim 17,
2A Prong 1:
“iteratively processing the input vectors to compute a knowledge map” A user mentally or with pencil and paper looks at data and connects it together into a knowledge map.
“iteratively processing the input vectors to determine metadata associated with one or more knowledge elements” The user mentally or with pencil and paper determines metadata about the knowledge elements of the knowledge graph.
“determining whether an input vector is within a knowledge element (KE) based on the knowledge map and the metadata” The user determines whether input data matches the elements of the knowledge map.
“When it is determined that the input vector is not within a KE, identify one or more neighboring KEs in a vicinity of the input vector” The user mentally or with pencil and paper looks to see if there are any neighboring KEs.
“Determining a probabilistic classification based on the identification of the one or more neighboring KEs” The user mentally or with pencil and paper determines the class based upon nearby KEs.
2A Prong 2: This judicial exception is not integrated into a practical application.
Additional elements:
A computer implemented (mere instructions to apply the exception using a generic computer component);
A learning process (Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f) – Examiner’s note: “learning process” is a generic machine learning algorithm with no particular limitations or details that make it anything more than an off the shelf, generic machine learning process.
“obtaining …, a plurality of input vectors” (Adding insignificant extra-solution activity to the judicial exception - see MPEP 2106.05(g)).
2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Additional elements:
A computer implemented (mere instructions to apply the exception using a generic computer component)
A learning process (Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f) – Examiner’s note: “learning process” is a generic machine learning algorithm with no particular limitations or details that make it anything more than an off the shelf, generic machine learning process.
“obtaining …, a plurality of input vectors” (MPEP 2106.05(d)(II) indicate that merely “receiving and transmitting data” is a well‐understood, routine, conventional function when it is claimed in a merely generic manner (as it is in the present claim). Thereby, a conclusion that the claimed obtaining step is well-understood, routine, conventional activity is supported under Berkheimer).
As per claims 18, 20, and 23, these claims contain similar mental steps to claim 17 and are rejected for similar reasons to claim 17.
As per claim 19, 21-22, and 24, these claims contain similar mental steps and generic hardware to claim 17 and are rejected for similar reasons to claim 17.
Claim Rejections - 35 USC § 112
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-2, 9-10, and 17-18, are rejected under 35 U.S.C. 103 as being unpatentable over Thomas et al (US 20150178631 A1) in view of Liang et al (US 20160042299 A1).
As per claims 1, 9, and 17, Thomas discloses, “A probabilistic” (pg.14, particularly paragraph 0148; EN: this denotes getting probabilistic results). “Classification system” (abstract; EN: this denotes the system being used for pattern recognition, a type of classification).
“a memory” (pg.31, particularly paragraph 0315; EN: this denotes the hardware for running the system).
“one or more processors; and” (pg.31, particularly paragraph 0315; EN: this denotes the hardware for running the system).
“logic operable to cause the one or more processors to:” (pg.31, particularly paragraph 0315; EN: this denotes the hardware for running the system).
“obtain, in association with a learning process” (Pg.8, particularly paragraph 0095; EN: this denotes training the system by creating the knowledge map with knowledge elements). “A plurality of input vectors” (Pg.8, particularly paragraph 0095; EN: this denotes providing input vectors with known categories to the model to train the system).
“iteratively process the input vectors to compute a knowledge map” (Pg.8, particularly paragraph 0095; EN: this denotes providing input vectors with known categories to the model to train the system).
“iteratively process the input vectors to determine data associated with one or more knowledge elements” (Pg.17, particularly paragraph 0175; EN: this denotes the knowledge elements having associated metadata).
“determine whether an input vector is within a knowledge element (KE) based on the knowledge map …” (Pg.19, particularly paragraph 0195; EN: this denotes matching input vectors to the knowledge elements of the system).
“when it is determined that the input vector is not within a KE, identify one or more neighboring KEs in a vicinity of the input vector” (pg.14-15, particularly paragraph 0154-0157; EN: this denotes the various ways the system responds to inputs, with the “indeterminate recognition” section states that the vector is in multiple fields (i.e. not within a single KE) and identifies nearby potential KEs that it is within the influence of as potential classifications).
“Determining a probabilistic classification based on the identification of the one or more neighboring KEs” (pg.14, particularly paragraph 0148; EN: this denotes getting probabilistic results. Pg.15, particularly paragraph 0157; EN: this denotes using the various neighboring KEs to make a classification).
However, Thomas fails to explicitly disclose, “determine whether an input vector is within a knowledge element (KE) Based on the knowledge map and the data.”
Liang discloses, “determine whether an input vector is within a knowledge element (KE) Based on the knowledge map and the data” (Pg.11-12, particularly paragraph 0113; EN: this denotes using metadata about knowledge elements to help in matching).
Thomas and Liang are analogous art because both involve knowledge maps.
Before the effective filing date it would have been obvious to one skilled in the art of knowledge maps to combine the work of Thomas and Liang in order to use metadata to match incoming data to knowledge elements.
The motivation for doing so would be to allow the system to “store[] based on the identified key terms and metadata to make the knowledge units searchable in a knowledge bank” (Liang, Pg.4, particularly paragraph 0054) or in the case of Thomas, allow the system to search for matches based on the metadata along with other data as needed.
Therefore before the effective filing date it would have been obvious to one skilled in the art of knowledge maps to combine the work of Thomas and Liang in order to use metadata to match incoming data to knowledge elements.
As per claims 2, 10, and 18, Thomas discloses, “wherein the probabilistic classification is set to have a probability of 1 if the input vector falls within an influence sphere of a specific KE or the input vector does not fall within an influencing sphere of any specific KE and any neighboring KEs in the vicinity of the input belong to the same class” (pg.9, particularly paragraph 0101; EN: This denotes using a hypersphere to determine the classification, including exact recognition, not recognized or indeterminate recognition. The choice of a number of value to be determined when there is an exact match is non-functional descriptive material).
As per claim 25, Thomas discloses, “wherein the logic and the one or more processors are implemented in one or more of a cloud-based computing architecture, an edge computing architecture, a local computing architecture, or any combination thereof” (pg.32, particularly paragraph 0323; EN: this denotes the use of cloud computing as an option for controlling the system).
Claim Rejections - 35 USC § 103
Claims 3-4, 6-7, 11-12, 14-15, 19-20, and 22-23, are rejected under 35 U.S.C. 103 as being unpatentable over Thomas et al (US 20150178631 A1) in view of Liang et al (US 20160042299 A1) and further in view of Bokser (US 4773099 A).
As per claims 3, 11, and 19, Thomas discloses, “wherein when the input vector does not fall within any specific KE” (Pg.15, particularly paragraph 0157; EN: this denotes the input falling outside the knowledge elements). “… along with … probabilities” (pg.14, particularly paragraph 0148; EN: this denotes getting probabilistic results).
However, Thomas fails to explicitly disclose, “the logic is operable to cause the one or more processors to identify two or more Kes to which the input may belong, along with corresponding probabilities.”
Bokser discloses, “the logic is operable to cause the one or more processors to identify two or more Kes to which the input may belong, along with corresponding probabilities” (C25, particularly L42-52; EN: this denotes determining confidence values (i.e. probabilities) that the incoming character is associated with a particular cluster).
Thomas and Bokser are analogous art because both involve multidimensional classification.
Before the effective filing date it would have been obvious to one skilled in the art of multidimensional classification to combine the work of Thomas and Bokser in order to assign probabilities to input when there is not an exact match.
The motivation for doing so would be because “The possibility set created contains, in addition to a list of character candidates, a corresponding list of confidences which can be used to flag characters which were not recognized with certainty so that they can be examined by a word processing operator” or in the case of Thomas, allow the system to assign confidence values to potential classes of the incoming data in order to determine the best matches to that data.
Therefore before the effective filing date it would have been obvious to one skilled in the art of multidimensional classification to combine the work of Thomas and Bokser in order to assign probabilities to input when there is not an exact match.
As per claims 4, 12, and 20, Bokser discloses, “Wherein the corresponding probabilities are a function of one or more of a distance of the input vector to an ith KE sphere, a number of input vectors that hit the ith KE sphere in a predetermine time window, a size of an influence distance of the ith KE sphere, a weighting function of the ith KE sphere, or a quality function of the ith KE” (C20, L46-65; EN: this denotes using distance for confidence determinations).
As per claims 6, 14, and 22, Thomas discloses, “determine a classification probability value” (pg.14, particularly paragraph 0148; EN: this denotes getting probabilistic results). “based on the knowledge map…” (Pg.19, particularly paragraph 0195; EN: this denotes matching input vectors to the knowledge elements of the system).
“determine an action based on the determined classification probability value” (Pg.30, particularly paragraph 0304; EN: this denotes the system responding to detected patterns). “if the probability value exceeds a predetermined threshold” (Pg.10, particularly paragraph 0115; EN: this denotes using thresholds to detect the patterns).
Liang discloses, “based on the knowledge map and the data” (Pg.11-12, particularly paragraph 0113; EN: this denotes using metadata about knowledge elements to help in matching).
AS per claims 7, 15, and 23, Thomas discloses, “Wherein the action includes one or more of alerting a user device regarding determined probabilities, restarting a system, or identifying an object as belonging to a specific class” (Pg.30, particularly paragraph 0304; EN: this denotes the system responding to detected patterns).
Claim Rejections - 35 USC § 103
Claims 5, 8, 13, 16, 21 and 24, are rejected under 35 U.S.C. 103 as being unpatentable over Thomas et al (US 20150178631 A1) in view of Liang et al (US 20160042299 A1) and Bokser (US 4773099 A) and further in view of Hertzmann et al (“Classification”).
As per claim 5, 13, and 21, Bokser discloses, “identify input vectors which belong to a first KE and a second KE with corresponding probabilities” (C25, particularly L42-52; EN: this denotes determining confidence values (i.e. probabilities) that the incoming character is associated with a particular cluster. As this is a scale of probabilities that has values regularly put through it, any input vector can have the same probability as another one if the calculations come out the same).
However, Thomas and Bokser fail to explicitly disclose, “determine a multi-dimensional plane separating the first KE and the second KE based on the identified input vectors.”
Hertzmann discloses, “determine a multi-dimensional plane separating the first KE and the second KE based on the identified input vectors” (Pg.42, Classification section, fourth paragraph; EN: this denotes the goal of classification being determining a decision boundary where points on the boundary have an equally probably chance of being in either class).
Hertzmann and Thomas modified by Bokser are analogous art because both involve classification.
Before the effective filing date it would have been obvious to one skilled in the art of classification to combine the work of Hertzmann and Thomas modified by Bokser in order to use equal probability values to determine the plane between two classes.
The motivation for doing so would be to “identify the regions of the input space the correspond to each class” (Hertzmann, Pg.42, fourth paragraph) or in the case of Thomas modified by Bokser, allow the system to determine the boundaries between the different classifications as needed.
Therefore before the effective filing date it would have been obvious to one skilled in the art of classification to combine the work of Hertzmann and Thomas modified by Bokser in order to use equal probability values to determine the plane between two classes.
As per claims 8, 16, and 24, Thomas discloses, “wherein when the input vector does not fall within any specific KE, the logic is operable to cause the one or more processors to” (Pg.15, particularly paragraph 0157; EN: this denotes the input falling outside the knowledge elements).
Bokser discloses, “identify two or more Kes to which the input vector may belong” (C25, particularly L42-52; EN: this denotes determining confidence values (i.e. probabilities) that the incoming character is associated with a particular cluster).
However, Thomas modified by Bokser fails to explicitly disclose, “identify that the neighboring Kes belong to the same class; and “… determine that the input vector belongs to the class with a probability of 1.”
Hertzmann discloses, “identify that the neighboring Kes belong to the same class; and “… determine that the input vector belongs to the class with a probability of 1” (Pg.46-47, particularly section 8.4; EN: this denotes looking at nearest neighbors for classification. If the majority of nearest neighbors are the same class, then the new data will get the same class. As discussed above, labeling the value as a specific number is non-function descriptive material).
Hertzmann and Thomas modified by Bokser are analogous art because both involve classification.
Before the effective filing date it would have been obvious to one skilled in the art of classification to combine the work of Hertzmann and Thomas modified by Bokser in order to consider multiple knowledge elements of the same class in determining classification.
The motivation for doing so would be to “allow the number of nearest neighbors (i.e., K) we are effectively smoothing the decision boundary, hopefully thereby improving generalization” (Hertzmann, Pg.47, second paragraph) or in the case of Thomas, allow the system to consider different knowledge elements of the same class when determining if incoming data is matched to a particular class.
Therefore before the effective filing date it would have been obvious to one skilled in the art of classification to combine the work of Hertzmann and Thomas modified by Bokser in order to consider multiple knowledge elements of the same class in determining classification.
Response to Arguments
In pg.8, the Applicant argues in regards to the rejection under U.S.C. 101,
As discussed, Applicant disputes under Prong One of the USPTO Step 2A analysis that independent claim 1 recites a law of nature, natural phenomenon or abstract idea. For instance, independent claim 1 does not recite subject matter which can be categorized as mathematical concepts, certain methods of organizing human activity and/or mental processes. Independent claim 1 instead recites a probabilistic classification system implemented with a memory and one or more processors as well as logic operable to cause the one or more processors to perform processing techniques in relation to iterative processing of input vectors in relation to one or more knowledge elements. For instance, when it is determined that an input vector is not within a knowledge element (KE), a processing operation includes identifying one or more neighboring KEs in a vicinity of the input vector, as recited in amended claim 1.
In response, the Examiner maintains the rejection as shown above. Applicant merely states that they use generic computer equipment such as memory and processors, and that determining whether input is within a knowledge is followed up by looking at nearby knowledge elements. The generic computer hardware is not significantly more than the abstract idea, and it is a mental step to see if input fits knowledge elements, and consider nearby knowledge elements if it does not as shown in the rejection above. Therefore the rejection is maintained as shown above.
In pg.8-9, the Applicant argues in regards to the rejection under U.S.C. 101,
Some implementations of independent claim 1 provide improvements in the functioning (including implementation, usefulness and performance) of a probabilistic classification system integrated into a practical application with a memory and one or more processors as well as logic operable to cause the one or more processors to perform various processing techniques. For instance, amended claim 1 recites: "determine whether an input vector is within a knowledge element (KE) based on the knowledge map and the data, when it is determined that the input vector is not within a KE, identify one or more neighboring KEs in a vicinity of the input vector, and determine a probabilistic classification based on the identification of the one or more neighboring KEs." As further explained in Applicant's specification, some non-limiting examples of the apparatus and operations of independent claim 1 can be implemented in one or more of a cloud-based computing architecture, an edge computing architecture, a local computing architecture, or any combination thereof.
In response, the Examiner maintains the rejection as shown above. The addition of more generic computer equipment such as cloud computing or the like does not change the concept of the claims describing an abstract idea placed upon generic computer equipment. Since the claim lacks significantly more than the abstract idea, the rejection is maintained as shown above.
In pg.11, Applicant argues in regards to the rejection of the independent claims under U.S.C. 103,
Liang fails to cure the elements missing in Thomas for the reasons discussed in the April 29th interview. For instance, while Liang teaches in paragraph 0113 the notion that a "knowledge matching service can be enhanced through analysis of metadata associated with the knowledge elements," Liang does not disclose or suggest any identifying of one or more neighboring KEs in a vicinity of an input vector. Liang instead teaches in paragraph 0113 that examples of metadata associated with knowledge elements are user comments and user ratings. As taught by Liang in paragraph 0113: "For example, a knowledge element that is matched to a particular knowledge consumer may nevertheless be not recommended to the user if the user ratings for that knowledge element is low." Independent claim 1, by contrast, is amended to recite: "determine whether an input vector is within a knowledge element (KE) based on the knowledge map and the data, when it is determined that the input vector is not within a KE, identify one or more neighboring KEs in a vicinity of the input vector, and determine a probabilistic classification based on the identification of the one or more neighboring KEs."
In response, the Examiner maintains the rejection as shown above. The new limitations are met by the Thomas reference, not the Liang reference. The Liang reference was brought in to show that additional data, such as metadata, can be considered when matching input to knowledge elements. Since Liang is not required to meet these limitations, the rejection is maintained as shown above.
Applicant's arguments with respect to claims 1-25 have been considered but are either conclusory or repetitions of the above arguments, and therefore rejected for similar reasons.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to BEN M RIFKIN whose telephone number is (571)272-9768. The examiner can normally be reached Monday-Friday 9 am - 5 pm.
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/BEN M RIFKIN/ Primary Examiner, Art Unit 2123
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