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 24 claims pending in the application, all of which are ready for examination by the examiner.
I. ACKNOWLEDGEMENT OF REFERENCES CITED BY APPLICANT
Information Disclosure Statement
As required by M.P.E.P 609(c), the applicant’s submissions of the Information Disclosure Statement dated 5/30/24 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.
The international search report was not provided to the Examiner, and therefore was lined through on the IDS and not considered at this time.
II. REJECTIONS NOT BASED ON PRIOR ART
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 1-24 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 determines whether input data matches the elements of the knowledge map.
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, and 8, 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.
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.
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
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 1-24 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.
As per claims 1, 9, and 17, these claims call for “iteratively process the input vectors to determine metadata associated with one or more knowledge elements.” However, the claims and specification at no time define or explain just what metadata is determined. Metadata is described in paragraphs 0008, 0013, 0169, 0184, 0191, and 0231-0233.
In paragraph 0008, the paragraph merely states the same as the claim with no clarifying detail.
In paragraph 0013, it merely states that classification is performed using the metadata.
Paragraph 0169 states that the metadata is updated when a vector falls within a specific knowledge element, and that newly created knowledge elements will have metadata, but never describes what the metadata actually is.
Paragraphs 0184, 0191 similarly to paragraph 0169 talks about updating the metadata when an input vector is determined to be a member.
Paragraphs 0231-0233 merely states that application metadata is part of the system and may be allocated setup of applications can be stored as metadata in tenant storage, but this appears unrelated to the metadata for knowledge elements.
As shown, the specification and claims never actually disclose just what “metadata” is determined for the knowledge vectors. This causes the claims to be unclear and lacking defined metes and bounds, as the metadata being used/determined is never defined or explained, and therefore it is impossible for the examiner to determine the metes and bounds of the claim. Therefore the claim is rejected under U.S.C. 112(b) for failing to particularly point out and claim the intended invention.
As per claims 2-8, 10-16, and 18-24, these claims are rejected as being dependent on a claim rejected under U.S.C. 112(b) for failing to particularly point out and claim the intended invention.
As per claims 5, 13, and 21, these claims call for “identify input vectors which belong to a first KE and a second KE with substantially equal probabilities.” The term “substantially” is a relative term which renders the claim indefinite. The term “substantially” is not defined by the claim, the specification does not provide a standard for ascertaining the requisite degree, and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention. The determination of the probabilities is rendered indefinite due to the relative term, and therefore rejected under U.S.C. 112(b) for failing to particularly point out and claim the intended invention.
III. REJECTIONS BASED ON PRIOR ART
Examiners Note: Some rejections will be followed by an ‘EN’ that will denote an examiners note. This will be placed to further explain a rejection.
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 metadata 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).
“Determining a probabilistic classification based on the determination” (pg.14, particularly paragraph 0148; EN: this denotes getting probabilistic results).
However, Thomas fails to explicitly disclose, “determine whether an input vector is within a knowledge element (KE) Based on the knowledge map and the metadata.”
Liang discloses, “determine whether an input vector is within a knowledge element (KE) Based on the knowledge map and the metadata” (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” (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).
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 metadata” (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 substantially equal 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.
Conclusion
The examiner requests, in response to this Office action, support be shown for language added to any original claims on amendment and any new claims. That is, indicate support for newly added claim language by specifically pointing to page(s) and line no(s) in the specification and/or drawing figure(s). This will assist the examiner in prosecuting the application.
When responding to this office action, Applicant is advised to clearly point out the patentable novelty which he or she thinks the claims present, in view of the state of the art disclosed by the references cited or the objections made. He or she must also show how the amendments avoid such references or objections See 37 CFR 1.111(c).
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.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Alexey Shmatov can be reached at (571) 270-3428. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/BEN M RIFKIN/Primary Examiner, Art Unit 2123