DETAILED ACTION
Notice relating to Pre-AIA or AIA Status
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 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 present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Status of the Claims
Applicant’s original claims (dated 05 JUNE 2025), are the ones currently being examined. The status of the claims is as follows: Claims 1-20 are currently pending in the application.
Examiner’s Note
It is noted to Applicant that Allowable subject matter has been indicated in related Application 17/652,875. Applicant is suggested to try and incorporate similar Allowable content into the current Application’s claims to try and move prosecution forward to an Allowance. Applicant is also cautioned not to repeat allowable subject matter in a manner that could lead to a double patenting rejection. This is just a note and suggestion by the Examiner, any amendments made by Applicant will be searched thoroughly before a final indication on Allowability is made.
Specification
The lengthy specification has not been checked to the extent necessary to determine the presence of all possible minor errors. Applicant’s cooperation is requested in correcting any errors of which applicant may become aware in the specification.
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 1-20 are rejected on the ground of nonstatutory double patenting as being unpatentable over at least claims 1-14, 16-17, and 19-20 of U.S. Patent No. 12,348,793. Although the claims at issue are not identical, they are not patentably distinct from each other because the current applications claims are only worded in a slightly differing and broader manner than the Patent claims, while still leading to the same inventive outcome/concept.
Claim Objections
Claim 9 is objected to because of the following informalities: the claim states “…wherein the extent of correlation of is indicated by the machine learning model.” The Examiner believes this is a typographical error, and should actually state “…wherein the extent of correlation is indicated by the machine learning model.”. 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.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claims recite (at least) determining a first rate at which computing devices has entered a device state and a second rate at which one or more computing devices has entered the device state, and generating an indication that the two rates differ. The limitations of “determining”, “selecting”, “generating”, and “training”, as drafted, are processes that, under a broadest reasonable interpretation, covers performance of the limitations in the mind but for the recitation of generic components. That is, other than reciting a “computing device”, a “machine learning model”, a “decision tree model”, a “processor”, and/or a “non-transitory computer-readable medium”, nothing in the claimed elements precludes the steps from practically being performed in the mind. For example, but for the recited language, “determining” in the context of this claim encompasses the user manually/mentally calculating data based on other observed information/data; “selecting” in the context of this claim encompasses the user manually/mentally choosing a device/element/value; “generating” in the context of this claim encompasses the user indicating via verbal or written indication about something they determined; and “training” in the context of this claim encompasses the user mentally learning based on current and previous knowledge or other observations. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claims recite an abstract idea.
This judicial exception is not integrated into a practical application. In particular, the claim only recites minor, generic, additional elements – a “computing device”, a “machine learning model”, a “decision tree model”, a “processor”, and/or a “non-transitory computer-readable medium”. These elements, throughout the steps, are recited at a high-level of generality (i.e., as a generic computing device, model, processor, and/or computer readable medium, being used to perform generic functions) such that they amount to no more than mere instructions to apply the exception using generic components. Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claims are directed to an abstract idea.
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements amount to no more than mere instructions to apply the exception using generic components. Mere instructions to apply an exception using generic components cannot provide an inventive concept. The claims are not patent eligible.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1-3, 5-14, and 16-20 are rejected under 35 U.S.C. 103 as being unpatentable over Sekar, US 2004/0098617 in view of Harutyunyan et al., US 2022/0027257.
Regarding claim 1, Sekar discloses a computer-implemented method comprising:
determining, using, machine learning (with at least machine learning; page 3, paragraphs 33 and 40) an attribute value of an attribute represented by reference data associated with a plurality of computing devices entering a first device state of a plurality of possible device states, wherein the attribute value is correlated with entry into the first device state (can utilize specification of attributes and their values when entering/transitioning specific states for training, wherein this is then used for further determinations and comparisons; page 2, paragraph 32, and Fig. 1, elements 101-104);
selecting, from a plurality of computing devices (based on multiple machines/instances going through various states; pages 1-2, paragraph 14, and page 2, paragraphs 23-24 and 32, and page 3, paragraph 40), a first computing device that is associated with the attribute value (based on a statistical property of interest, i.e. attribute, system can select particular machine(s)/instance(s), i.e. including at least a first for determining and comparing information; Fig. 1, element 106, and page 2, paragraph 32, and pages 1-2, paragraph 14);
determining, based on the reference data, a first measure indicative of a first rate at which the first computing device has entered the first device state (can at least determine rate/frequencies of transitioning between states, i.e. entering at least a first state; page 2, paragraphs 17 and 21, and page 4, paragraphs 42 and 45, and page 6, paragraph 87);
determining, a second measure indicative of a second rate at which a second computing device has entered the first device state (again, based on a statistical property of interest, i.e. attribute, system can select particular machine(s)/instance(s), i.e. including at least a second of the plurality, for determining and comparing information; Fig. 1, element 106, and page 2, paragraph 32, and pages 1-2, paragraph 14, and again with determination about rate/frequencies of transitioning between states, i.e. entering the at least first state; page 2, paragraphs 17 and 21, and page 4, paragraphs 42 and 45, and page 6, paragraph 87, wherein can also be based on determination about typical transitions for at least a machine, i.e. including a second; page 2, paragraph 32, and page 6, paragraphs 87 and 92); and
generating, based on a comparison of the first measure to the second measure, an indication that the second rate differs from the first rate (based on comparisons between the devices/data, when a rate/frequency of the machines differ, such that at least one is outside of typical/observed values, an alert, i.e. indication, can be generated; Fig. 1, elements 107 and 108, and page 2, paragraph 32, and page 1-2, paragraph 14, and page 2, paragraphs 23-24, and page 7, paragraph 97).
While Sekar does again disclose machine learning (with at least machine learning; page 3, paragraphs 33 and 40), Sekar does not explicitly disclose a machine learning model.
In a related art, Harutyunyan does disclose a machine learning model (system can utilize a trained machine learning model to make determinations/predictions; Fig. 47, element 4701, and Fig. 48, element 4806, and page 2, paragraphs 55-56, and page 24, paragraph 298, and page 25, paragraph 304, and page 26, paragraphs 317-318).
Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to combine the prior art of Sekar and Harutyunyan, by allowing machine learning models to be utilized with the already present machine learning disclosed in Sekar, in order to provide an improved system and method for troubleshooting performance problems in a distributed computing system (Harutyunyan; page 1, paragraph 2).
Regarding claim 2, Sekar in view of Harutyunyan discloses training the machine learning model based on the reference data (Sekar; can utilize specification of attributes and their values when entering/transitioning specific states for training, wherein this is then used for further determinations and comparisons; page 2, paragraph 32, and Fig. 1, elements 101-104, and page 6, paragraph 87, and Harutyunyan; system can utilize a trained machine learning model to make determinations/predictions; Fig. 47, element 4701, and Fig. 48, element 4806, and page 2, paragraphs 55-56, and page 24, paragraph 298, and page 25, paragraph 304, and page 26, paragraphs 317-318).
Regarding claim 3, Sekar in view of Harutyunyan discloses the reference data comprises respective values of a plurality of attributes associated with the plurality of computing devices (Sekar; including values associated with particular attributes for the devices transitioning the states; page 2, paragraph 32, and page 3, paragraph 39, and page 4, paragraphs 42 and 45, and again based on a plurality of machines/instances going through various states; pages 1-2, paragraph 14, and page 2, paragraphs 23-24 and 32, and page 3, paragraph 40);
training the machine learning model based on the reference data comprises training, using the reference data, a decision tree model to determine, based on the respective values of the plurality of attributes, a rate of entry of the plurality of computing devices into the first device state (Sekar; again, can be trained based on the reference data to determine rate/frequencies of transitioning between states, i.e. entering at least a first state; page 2, paragraphs 17 and 21, and page 3, paragraph 39, and page 4, paragraphs 42 and 45, and page 6, paragraph 87, and Harutyunyan; with use of decision tree model; page 25, paragraph 304); and
determining the attribute value further comprises: determining, based on a structure of the decision tree model, a combination of one or more values of the plurality of attributes that are correlated with entry into the first device state (Sekar; transitioning to the particular states based on correlation to certain values, i.e. at least a combination of one or more; page 2, paragraphs 17 and 21, and page 3, paragraph 39, and page 4, paragraphs 42 and 45, and page 6, paragraph 87, and Harutyunyan; operations can be based on a particular structure/type of decision tree; page 25, paragraph 304); and
selecting the attribute value of the attribute from the combination of the one or more values of the plurality of attributes that are correlated with entry into the first device state (Sekar; particular statistical property of interest, i.e. selected attribute, wherein is utilized for determining rate/frequencies of transitioning between states, i.e. entering at least a first state; page 2, paragraphs 17 and 21, and page 4, paragraphs 42 and 45, and page 6, paragraph 87).
Regarding claim 5, Sekar in view of Harutyunyan discloses determining a corresponding rate at which the plurality of computing devices represented by the reference data enter the first device state (Sekar; can utilize data to at least determine rate/frequencies of transitioning between states, i.e. entering at least a first state; page 2, paragraphs 17, 21, and 32, and page 4, paragraphs 42 and 45, and page 6, paragraph 87); and
training the machine learning model to approximate the corresponding rate based on values of the attribute represented by the reference data (Sekar; can utilize training data to determine rates/frequencies; page 2, paragraphs 17, 21, and 32, and page 4, paragraphs 42 and 45, and page 6, paragraph 87, and Harutyunyan; with machine learning model, and training in order to make predictions based on the data; Fig. 47, element 4701, and Fig. 48, element 4806, and page 2, paragraphs 55-56, and page 24, paragraph 298, and page 25, paragraphs 304-305, and page 26, paragraphs 317-318).
Regarding claim 6, Sekar in view of Harutyunyan discloses selecting, for the reference data, a corresponding classification from a plurality of predefined classifications by comparing (i) a corresponding rate at which the plurality of computing devices represented by the reference data enter the first device state to (ii) a threshold rate (Sekar; system can classify, i.e. select classification, for statistics of interest as being an anomaly or not, i.e. at least two types of classifications, based on compared rate/frequencies with at least a threshold; Fig. 1, elements 106-108, and page 2, paragraph 32, and with classes of abnormal transitions; page 5, paragraph 75); and
training the machine learning model to approximate the corresponding classification based on values of the attribute represented by the reference data (Sekar; based on trained machine learning, for detecting an anomaly or not, i.e. at least two types of classifications, based on compared rate/frequencies with at least a threshold; Fig. 1, elements 106-108, and page 2, paragraph 32, and with classes of abnormal transitions; page 5, paragraph 75, and Harutyunyan; with machine learning model, and training in order to make predictions based on the data; Fig. 47, element 4701, and Fig. 48, element 4806, and page 2, paragraphs 55-56, and page 24, paragraph 298, and page 25, paragraphs 304-305, and page 26, paragraphs 317-318).
Regarding claim 7, Sekar in view of Harutyunyan discloses a structure of the machine learning model represents the attribute value, and wherein a representation of the attribute value by the structure of the machine learning model is human-interpretable (Harutyunyan; attributes of the data represented with model being a decision tree of various forms/structures, i.e. decision tree is human-interpretable data; page 25, paragraph 304, and page 26, paragraph 315, and with specific attributes; page 20, paragraphs 244-249, and Sekar; based on at least a statistical property of interest, i.e. attribute; Fig. 1, element 106, and page 2, paragraph 32, and pages 1-2, paragraph 14).
Regarding claim 8, Sekar in view of Harutyunyan discloses the machine learning model comprises a decision tree model, and wherein the attribute value is represented by a hierarchy of a plurality of nodes of the decision tree model (Harutyunyan; attributes of the data represented with model being a decision tree of various forms/structures; page 25, paragraph 304, and page 26, paragraph 315, and with specific attributes; page 20, paragraphs 244-249, and wherein examples of nodes and their hierarchy; page 20, paragraph 250, and pages 20-21, paragraphs 255-257, and Sekar; based on at least a statistical property of interest, i.e. attribute; Fig. 1, element 106, and page 2, paragraph 32, and pages 1-2, paragraph 14).
Regarding claim 9, Sekar in view of Harutyunyan discloses selecting the attribute value from a plurality of values of the attribute based on an extent of correlation of the attribute value with entry into the first device state, wherein the extent of correlation of is indicated by the machine learning model (Sekar; including values associated with particular attributes for the devices transitioning the states; page 2, paragraph 32, and page 3, paragraph 39, and page 4, paragraphs 42 and 45, and again based on a plurality of machines/instances going through various states; pages 1-2, paragraph 14, and page 2, paragraphs 23-24 and 32, and page 3, paragraph 40, and Harutyunyan; based on rank ordered combination(s) of attributes/values, i.e. amount of correlation to the particular problem/state; page 25, paragraphs 310-311, and page 26, paragraph 317, and again with the machine learning model, and training in order to make predictions based on the data; Fig. 47, element 4701, and Fig. 48, element 4806, and page 2, paragraphs 55-56, and page 24, paragraph 298, and page 25, paragraphs 304-305, and page 26, paragraphs 317-318).
Regarding claim 10, Sekar in view of Harutyunyan discloses the attribute value of the attribute comprises a combination of a plurality of values of a plurality of attributes represented by the reference data (Sekar; including values associated with particular attributes for the devices transitioning the states; page 2, paragraph 32, and page 3, paragraph 39, and page 4, paragraphs 42 and 45, and again based on a plurality of machines/instances going through various states; pages 1-2, paragraph 14, and page 2, paragraphs 23-24 and 32, and page 3, paragraph 40, and Harutyunyan; based on combination(s) of attributes/values; page 25, paragraphs 310-311, and page 26, paragraph 317), wherein the combination of the plurality of values defines an order of two or more attributes of the plurality of attributes, wherein the order defines a relative correlation of each attribute of the two or more attributes with entry into the first device state, and wherein generating the indication comprises generating a representation of the order (Harautyunyan; system can rank order based on the combination(s) of attributes/values, wherein the highest ranked would be the indication of correlation to the particular state/problem, wherein the determinations are based on this ranking; page 25, paragraphs 310-311, and page 26, paragraph 317, and Sekar; alert, i.e. indication, can be generated; Fig. 1, elements 107 and 108, and page 2, paragraph 32, and page 1-2, paragraph 14, and page 2, paragraphs 23-24, and page 7, paragraph 97).
Regarding claim 11, Sekar in view of Harutyunyan discloses the machine learning model is configured to indicate that (i) the attribute value, when associated with at least one computing device, is correlated with the at least one computing device entering the first device state and (ii) a second value of the attribute, when associated with the at least one computing device, is correlated with the at least one computing device avoiding the first device state (Sekar; based on the particular values/attributes for the particular instance(s)/machine(s), the machine learning can correlate it with the device being in abnormal/anomaly state or not, i.e. avoiding the abnormal/anomaly state; Fig. 1, elements 106-108, and page 2, paragraph 32, and can include other classes of abnormal transitions; page 5, paragraph 75, and Harutyunyan; again with the machine learning model, and training in order to make predictions based on the data; Fig. 47, element 4701, and Fig. 48, element 4806, and page 2, paragraphs 55-56, and page 24, paragraph 298, and page 25, paragraphs 304-305, and page 26, paragraphs 317-318).
Regarding claim 12, Sekar in view of Harutyunyan discloses determining the attribute value comprises determining a combination of values of a plurality of attributes represented by the reference data, wherein the combination of values is correlated with entry into the first device state (Sekar; can utilize specification of attributes and their values when entering/transitioning specific states, wherein this is then used for further determinations and comparisons; page 2, paragraph 32, and Fig. 1, elements 101-104, and again with different combination(s) of values associated with particular attributes for the devices transitioning the states; page 2, paragraph 32, and page 3, paragraph 39, and page 4, paragraphs 42 and 45);
selecting the first computing device comprises selecting a first computing device subset from the plurality of computing devices, wherein the first computing device subset comprises two or more computing devices, and wherein each respective computing device of the first computing device subset is associated with the combination of values (Sekar; based on a statistical property of interest, i.e. attribute, system can select particular machine(s)/instance(s), i.e. including at least a two or more for determining and comparing information; Fig. 1, element 106, and page 2, paragraph 32, and pages 1-2, paragraph 14, and wherein of multiple machines/instances going through various states; pages 1-2, paragraph 14, and page 2, paragraphs 23-24 and 32, and page 3, paragraph 40);
determining the first measure comprises determining, based on a first reference data subset of the reference data, the first measure indicative of a first rate at which computing devices of the first computing device subset have entered the first device state (Sekar; can at least determine rate/frequencies of transitioning between states, i.e. entering at least a first state; page 2, paragraphs 17 and 21, and page 4, paragraphs 42 and 45, and page 6, paragraph 87, and again of multiple machines/instances going through various states; pages 1-2, paragraph 14, and page 2, paragraphs 23-24 and 32, and page 3, paragraph 40);
determining the second measure comprises determining the second measure based on production data corresponding to the second computing device, wherein the second computing device is associated with the combination of values (Sekar; again, based on a statistical property of interest, i.e. attributes/values, system can select particular machine(s)/instance(s), i.e. including at least a second of the plurality, for determining and comparing information, i.e. production type data/information; Fig. 1, element 106, and page 2, paragraph 32, and pages 1-2, paragraph 14, and again with determination about rate/frequencies of transitioning between states, i.e. entering the at least a first state; page 2, paragraphs 17 and 21, and page 4, paragraphs 42 and 45, and page 6, paragraph 87, wherein can also be based on determination about typical transitions for at least a machine, i.e. including a second; page 2, paragraph 32, and page 6, paragraphs 87 and 92); and
generating the indication comprises generating, based on the comparison of the first measure to the second measure, an indication that the second rate differs from the first rate by more than a predefined threshold amount (Sekar; based on comparisons between the devices/data, when a rate/frequency of the machines differ, such that at least one is outside of typical/observed values, i.e. outside a threshold, an alert, i.e. indication, can be generated; Fig. 1, elements 107 and 108, and page 2, paragraph 32, and page 1-2, paragraph 14, and page 2, paragraphs 23-24, and page 7, paragraph 97).
Regarding claim 13, Sekar in view of Harutyunyan discloses the first device state represents an abnormal device state in which the first computing device operates abnormally (Sekar; state can be an anomaly/abnormal state; Fig. 1, element 108, and page 2, paragraph 32, and page 5, paragraph 75, and page 7, paragraph 102, and Harutyunyan; can include abnormal states; page 13, paragraph 155).
Regarding claim 14, Sekar in view of Harutyunyan discloses the reference data corresponds to a first time period that represents operation of the first computing device before a change in one or more values of one of more attributes represented by the reference data (Sekar; can at least determine rate/frequencies of transitioning between states, i.e. entering at least a first state; page 2, paragraphs 17 and 21, and page 4, paragraphs 42 and 45, and page 6, paragraph 87, and wherein data can be based on statistical data; page 4, paragraphs 42-46, including frequency distributions; page 2, paragraphs 17-18, 23-24, and 32, and page 6, paragraphs 87-88, and Harutyunyan; based on a first time period before a change; page 11, paragraph 127, and page 13, paragraph 145, and page 18, paragraph 196), and wherein the second measure corresponds to a second time period that represents operation of the second computing device after the change in the one or more values of the one of more attributes (Sekar; again, based on a statistical property of interest, i.e. attribute, system can select particular machine(s)/instance(s), i.e. including at least a second of the plurality, for determining and comparing information; Fig. 1, element 106, and page 2, paragraph 32, and pages 1-2, paragraph 14, and again with determination about rate/frequencies of transitioning between states, i.e. entering the at least a first state; page 2, paragraphs 17 and 21, and page 4, paragraphs 42 and 45, and page 6, paragraph 87, wherein can also be based on determination about typical transitions for at least a machine, i.e. including a second; page 2, paragraph 32, and page 6, paragraphs 87 and 92, and wherein the data can be based on statistical data; page 4, paragraphs 42-46, including frequency distributions; page 2, paragraphs 17-18, 23-24, and 32, and page 6, paragraphs 87-88, and Harutyunyan; based on a second time period after a change; page 11, paragraph 127, and page 13, paragraph 145, and page 18, paragraph 196).
Regarding claim 16, Sekar in view of Harutyunyan discloses the first measure comprises a first parameter of a first statistical distribution that represents the first rate (Sekar; can at least determine rate/frequencies of transitioning between states, i.e. entering at least a first state; page 2, paragraphs 17 and 21, and page 4, paragraphs 42 and 45, and page 6, paragraph 87, and wherein data can be based on statistical data; page 4, paragraphs 42-46, including frequency distributions; page 2, paragraphs 17-18, 23-24, and 32, and page 6, paragraphs 87-88), wherein the second measure comprises a second parameter of a second statistical distribution that represents the second rate (Sekar; again, based on a statistical property of interest, i.e. attribute, system can select particular machine(s)/instance(s), i.e. including at least a second of the plurality, for determining and comparing information; Fig. 1, element 106, and page 2, paragraph 32, and pages 1-2, paragraph 14, and again with determination about rate/frequencies of transitioning between states, i.e. entering the at least a first state; page 2, paragraphs 17 and 21, and page 4, paragraphs 42 and 45, and page 6, paragraph 87, wherein can also be based on determination about typical transitions for at least a machine, i.e. including a second; page 2, paragraph 32, and page 6, paragraphs 87 and 92, and wherein the data can be based on statistical data; page 4, paragraphs 42-46, including frequency distributions; page 2, paragraphs 17-18, 23-24, and 32, and page 6, paragraphs 87-88), and wherein the comparison of the first measure to the second measure comprises determining a disparity measure that represents a disparity between the first statistical distribution and the second statistical distribution (Sekar; based on comparisons between the devices/data, when a rate/frequency of the machines differ, such that at least one is outside of typical/observed values, i.e. a disparity in the data, an alert, i.e. indication, can be generated; Fig. 1, elements 107 and 108, and page 2, paragraph 32, and page 1-2, paragraph 14, and page 2, paragraphs 23-24, and page 7, paragraph 97, and again the differences in the data can be based on statistical data; page 4, paragraphs 42-46, including frequency distributions; page 2, paragraphs 17-18, 23-24, and 32, and page 6, paragraphs 87-88).
Regarding claim 17, Sekar in view of Harutyunyan discloses the indication that the second rate differs from the first rate comprises an identification of (i) the second computing device and (ii) a disparity between the second rate and the first rate (Sekar; alert can be used to identify the source, i.e. second device, and/or the nature, i.e. disparity; page 2, paragraph 32, and Harutyunyan; information can identify a particular computer and/or type of event; page 16, paragraphs 176-177).
Claim 18, which discloses a system, is analyzed with respect to the citations and/or rationale provided in the rejection of similar claim 1. The following additional limitations are also disclosed:
a processor (Sekar; including at least a processor; page 3, paragraph 34, and page 7, paragraph 104).
Claim 19, which discloses a system, is analyzed with respect to the citations and/or rationale provided in the rejection of similar claim 3.
Claim 20, which discloses a non-transitory computer-readable medium, is analyzed with respect to the citations and/or rationale provided in the rejection of similar claim 1. The following additional limitations are also disclosed:
a non-transitory computer-readable medium having stored thereon instructions that, when executed by a computing device, cause the computing device to perform operations (Sekar; including machine executable program instruction on a storage device; page 2, paragraph 24, and page 3, paragraph 36, and Harutyunyan; with at least machine readable instructions stored on a physical data-storage device, i.e. non-transitory; page 5, paragraph 80, and page 26 paragraph 316, and a non-transitory computer-readable medium having executable instructions; see at least claim 21).
Claim 15 is rejected under 35 U.S.C. 103 as being unpatentable over Sekar, US 2004/0098617 in view of Harutyunyan et al., US 2022/0027257 and further in view of Hauser et al., US 2019/0354153.
Regarding claim 15, Sekar in view of Harutyunyan discloses all the claimed limitations of claim 14, as well as the change in the one or more values of the one of more attributes (Sekar; changes in values; page 6, paragraph 88, and Harutyunyan; changes in values from a time before and a time after an event/change point; page 11, paragraph 127, and page 13, paragraph 145, and page 18, paragraph 196).
Sekar in view of Harutyunyan does not explicitly disclose change is caused by release of an update.
In a related art, Hauser does disclose change is caused by release of an update (system can exhibit change in states based on receiving an update, i.e. released update; page 2, paragraph 34, and pages 2-3, paragraph 35).
Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to combine the prior art of Sekar, Harutyunyan, and Hauser by allowing certain events/change points for detection of anomalies, to include update releases, in addition to the already present events/change points disclosed in Sekar in view of Harutyunyan, in order to provide an improved system and method for monitoring the state transitions which occur when device processes are running to thereby improve security and operation of the devices (Hauser; page 1, paragraphs 2 and 5).
Conclusion
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/RANDY A FLYNN/Primary Examiner, Art Unit 2424
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