Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Priority
The present application is a continuation of application 16/029,106, now US Patent 11,610,107.
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 3, 10, and 17 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.
The term preferred in Claims 3, 10, and 17 is a relative term which renders the claim indefinite. The term preferred 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. It is thus unclear whether the inclusion of the word preferred modifies the claim scope or not. For the purpose of examination, the claim will be interpreted as if all predictions predict a preferred value inherently, as they are the outputs of the predictions.
The following is a quotation of 35 U.S.C. 112(d):
(d) REFERENCE IN DEPENDENT FORMS.—Subject to subsection (e), a claim in dependent form shall contain a reference to a claim previously set forth and then specify a further limitation of the subject matter claimed. A claim in dependent form shall be construed to incorporate by reference all the limitations of the claim to which it refers.
The following is a quotation of pre-AIA 35 U.S.C. 112, fourth paragraph:
Subject to the following paragraph [i.e., the fifth paragraph of pre-AIA 35 U.S.C. 112], a claim in dependent form shall contain a reference to a claim previously set forth and then specify a further limitation of the subject matter claimed. A claim in dependent form shall be construed to incorporate by reference all the limitations of the claim to which it refers.
Claim 3, 10, and 17 are rejected under 35 U.S.C. 112(d) or pre-AIA 35 U.S.C. 112, 4th paragraph, as being of improper dependent form for failing to further limit the subject matter of the claim upon which it depends, or for failing to include all the limitations of the claim upon which it depends. Each of the newly recited limitations in the dependent claims are already required by their respective parent. For example, wherein the prediction mapping is used to predict a preferred value for a knowledge data property in one or more knowledge data units is already required by the limitation updating the one or more knowledge data units … with one or more specific prediction values predicted by the prediction mapping.
Applicant may cancel the claim(s), amend the claim(s) to place the claim(s) in proper dependent form, rewrite the claim(s) in independent form, or present a sufficient showing that the dependent claim(s) complies with the statutory requirements.
Claim Rejections - 35 USC § 102
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 the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claims 1, 3-6, 7; 8, 10-14; 15, and 17-20 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Zhang et al., “Semantic Framework of Internet of Things for Smart Cities: Case Studies”.
Regarding Claim 1, Zhang teaches a computer-implemented method comprising: … by an artificial intelligence (AI) knowledge data server … (Zhang, pg. 8, Section 4.1, “The experimental setup was a SPARK cluster comprising four machines”) from an internet-of-things (IOT) device over one or more computer networks, a specific computer message that includes a specific set of IOT device-generated data items (Zhang, pg. 3, last paragraph & pg. 4, Fig. 1, “physical entities collect raw data in real-time from social media and physical sensors … the data is received by the abstract entities layer” [of their computing system] “This layer hides the complexity of devices providing a standard format to represent data from all kinds of devices”); identifying, based at least in part on the specific set of IOT device-generated data items, one or more knowledge data units from among a plurality of knowledge data units maintained in a knowledge data repository (Zhang, pg. 6, 1st paragraph, “For each block of smart cities, knowledge fusion is aimed at aggregating multiple disperse resources” where all the data for a block is a knowledge data unit, i.e. 2nd paragraph, “facts such as (Blockid, Has Air Quality, Good)”) wherein the plurality of knowledge data units in the knowledge data repository is generated at least partially through machine learning with a machine learning model implemented by a computing device; using a plurality of sets of IOT device-generated items including the specific set of IOT device-generated items to generate a plurality of samples and a corresponding plurality of observed values; generating a prediction mapping that maps the plurality of samples to the corresponding plurality of observed values; updating the one or more knowledge data units in the knowledge data repository with one or more specific prediction values predicted by the prediction mapping (Zhang, pg. 6, last paragraph, “The task of transfer learning involves transferring knowledge from rich data regions to regions with sparse data … we adopt transfer-learning technologies to enrich the feature representation for regions experiencing the data sparsity problem … to construct a feature mapping from an original instance to a hidden representation” where populating sparse blocks with latent representations and learned reconstructions over time is updating and generating the knowledge data units using machine learning aby using samples and observed values, i.e. see Fig. 4 for inputs and reconstructed values to learn) and causing the one or more knowledge data units to be used by an AI knowledge data query processor implemented using one or more computer devices to knowledge data query requests from client computing devices (Zhang, pg. 4, 1st paragraph, “if the user requires a forecast of the traffic conditions
y
i
… of street block
i
, the traffic-related information … is fused into feature vectors through urban knowledge fusion, and this is used to train a model to predict the result
y
i
”).
Regarding Claim 3, Zhang teaches the method of Claim 1 (and thus the rejection of Claim 1 is incorporated). As noted in the 35 U.S.C. 112(d) rejection of Claim 3, as Claim 3 fails to further narrow the scope of Claim 1, Claim 3 is rejected for reasons set forth in the rejection of Claim 1.
Regarding Claim 4, Zhang teaches the method of Claim 3 (and thus the rejection of Claim 3 is incorporated). Zhang further teaches wherein the preferred value for the knowledge property is generated using one or more machine learning methods that comprise one or more of … classification-based machine learning methods (Zhang, pg. 4, 1st paragraph, “a forecast of the traffic conditions” where the forecast is one of Clear, SlowSpeed, and Jam is a classification into one of the three category labels).
Regarding Claim 5, Zhang teaches the method of Claim 3 (and thus the rejection of Claim 3 is incorporated). Zhang further teaches wherein the preferred value of the knowledge data property is generated by minimizing an objective function measuring prediction accuracy (Zhang, pg. 8, last paragraph, “we adopt the root mean square error … where
y
^
i
is a prediction and
y
i
is the ground truth”).
Regarding Claim 6, Zhang teaches the method of Claim 1 (and thus the rejection of Claim 1 is incorporated). Zhang further teaches wherein the prediction mapping is used to update an existing knowledge data unit in the knowledge data repository (Zhang, pg. 3, final paragraph, “collect raw data in real-time from social media and physical sensors … the data take the form of continuous stream” with pg. 4, Fig. 1 denotes that the knowledge is updated over time, including knowledge from the prediction mapping in order to make the traffic and pollution prediction).
Regarding Claim 7, Zhang teaches the method of Claim 1 (and thus the rejection of Claim 1 is incorporated). Zhang further teaches wherein the prediction mapping is used to generate a new knowledge data unit in the knowledge data repository (Zhang, pg. 4, 2nd paragraph, “if a user requests the air quality of a particular street block, a new entity is generated as a result of the aggregation of data from related sensor and social media”).
Claims 8 and 10-14 recite a non-transitory computer readable medium that stores computer instructions to perform precisely the methods of Claims 1 and 3-7, respectively. As Zhang performs their method on a computer (Zhang, pg. 8, Section 4.1, “The experimental setup was a SPARK cluster comprising four machines”) in which such a medium is inherent, Claims 8 and 10-14 are rejected for reasons set forth in the rejections of Claims 1 and 3-7, respectively.
Similarly, Claims 15 and 17-20 recite an apparatus comprising: one or more computing processors; and a non-transitory computer readable medium to perform precisely the methods of Claims 1, 3, and 5-7, respectively. As Zhang performs their method on a computer, Claims 15 and 17-20 are also rejected for reasons set forth in the rejections of Claims 1, 3, and 5-7, respectively.
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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 2, 9, and 16 are rejected under 35 U.S.C. 103 as being unpatentable over Le-Phuoc et al., “The Graph of Things: A step towards the Live Knowledge Graph of connected things”.
Regarding Claim 2, Zhang teaches the method of Claim 1 (and thus the rejection of Claim 1 is incorporated). Zhang is silent regarding whether the plurality of samples and the corresponding plurality of observed values are maintained as historical data at the AI knowledge data server, but Le-Phuoc, in the analogous art of IoT knowledge graph data, teaches this limitation (Le-Phuoc, pg. 26, 3rd paragraph, “a scalable and elastic solution for ingesting, storing, exploring, and querying billions of dynamic IoT data points”). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to store the data streams of Zhang in a manner such as that of Le-Phuoc. The motivation to do so is to allow “the effective exploitation of Linked Stream Data from multiple sources” (Le-Phuoc, pg. 26, 3rd paragraph), which Zhang requires in order to operate.
Claim 9 recites a non-transitory computer readable medium that stores computer instructions to perform precisely the methods of Claim 2. As Zhang performs their method on a computer (Zhang, pg. 8, Section 4.1, “The experimental setup was a SPARK cluster comprising four machines”) in which such a medium is inherent, Claim 9 is rejected for reasons set forth in the rejection of Claim 2.
Similarly, Claim 16 recites an apparatus comprising: one or more computing processors; and a non-transitory computer readable medium to perform precisely the methods of Claim 2. As Zhang performs their method on a computer, Claim 16 is also rejected for reasons set forth in the rejections of Claim 2.
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, 3, 4, 6, 7; 8, 10, 11, 13, 14; 15, 17, 19 and 20 are rejected on the ground of nonstatutory double patenting as being unpatentable over their respective claims of U.S. Patent No. 11,610,107. Although the claims at issue are not identical, they are not patentably distinct from each other because the instant claims are strictly broader than the reference claims. See chart below.
Claims 2, 9, and 16 are rejected on the ground of nonstatutory double patenting as being unpatentable over their respective claims of U.S. Patent No. 11,610,107, in view of Le-Phuoc et al., “The Graph of Things: A step towards the Live Knowledge Graph of connected things.”
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to store the IoT data points, as does Le-Phuoc. The motivation to do so is to have them in storage for use in querying or other uses.
Claims 5, 12, and 18 are rejected on the ground of nonstatutory double patenting as being unpatentable over their respective claims of U.S. Patent No. 11,610,107, in view of Zhang et al., “Semantic Framework of Internet of Things for Smart Cities: Case Studies”.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to use RMSE in particular to measure the “quality of predicted values.” The motivation to do so is that RMSE is a well-known measure of error.
Instant Application 18/116,675
Reference Patent 11,610,107
Claim 1: A computer-implemented method comprising:
receiving, by an artificial intelligence (AI) knowledge data server from an internet-of-things (IOT) device over one or more computer networks, a specific computer message that includes a specific set of IOT device-generated data items;
identifying, based at least in part on the specific set of IOT device-generated data items, one or more knowledge data units from among a plurality of knowledge data units maintained in a knowledge data repository,
wherein the plurality of knowledge data units in the knowledge data repository is generated at least partly through machine learning with a machine learning model implemented by a computing device;
using a plurality of sets of IOT device-generated data items including the specific set of IOT device-generated data items to generate a plurality of samples and a corresponding plurality of observed values;
generating a prediction mapping that maps the plurality of samples to the corresponding plurality of observed values;
updating the one or more knowledge data units in the knowledge data repository with one or more specific prediction values predicted by the prediction mapping;
and causing the one or more knowledge data units to be used by an AJ knowledge data query processor implemented using one or more computer devices to generate knowledge data responses to knowledge data query requests from client computing devices.
Claim 1: A computer-implemented method comprising:
receiving a neural feedback from a client computing device over one or more computer networks … containing numeric measurement data of temperatures collected from one or more sensors
searching, based at least in part on a global neural schema and the one or more feedback keywords generated from the neural feedback, for one or more knowledge neurons in a repository of knowledge neurons
wherein the one or more knowledge neurons include knowledge artifacts learned … through machine learning with a machine learning model implemented by a computing device
using a plurality of neural feedbacks including the received neural feedback to generate a plurality of historic feedback datapoints comprising a plurality of samples and a corresponding plurality of observed values
generating a prediction mapping that maps the plurality of samples to the corresponding plurality of observed values;
updating at least one neuron in the repository of knowledge neurons with one or more specific prediction values predicted by the prediction mapping
and causing the one or more knowledge neurons to be used by a query processor in one or more computer devices to generate responses to query requests from client computing devices
Claim 2: The method of Claim 1, wherein the plurality of samples and the corresponding plurality of observed values are maintained as historical data
at the AJ knowledge data server.
Claim 2: wherein the neural feedback information as derived from the neural feedback, is recorded as one or more updated histories
Le-Phuoc, pg. 26, 3rd paragraph, storing dynamic IoT data
Claim 3. The method of Claim 1, wherein the prediction mapping is used to predict a preferred value for a knowledge data property in the one or more knowledge data units.
Obvious over Claim 1 of the reference patent as it fails to further narrow the scope the instant application’s Claim 1
Claim 4. The method of Claim 3, wherein the preferred value for the knowledge data property is generated using one or more machine learning methods that comprise one or more of: regression-based machine learning methods, classification-based machine learning methods, decision-tree-based machine learning methods, or random-forest-based machine learning methods.
Claim 4: The method of Claim 3, wherein the preferred value for the property is generated using one or more other machine learning methods[that] comprises one or more of: regression-based machine learning methods, classification-based machine learning methods, decision-tree- based machine learning methods, or random-forest-based machine learning methods.
Claim 5. The method of Claim 3, wherein the preferred value for the knowledge data property is generated by minimizing an objective function measuring
prediction accuracy.
Claim 5: herein the preferred value for the property is generated by minimizing an objective function measuring a quality of predicted values.
Zhang: pg. 8, RMSE
Claim 6. The method of Claim 1, wherein the prediction mapping is used to update an existing knowledge data unit in the knowledge data repository.
Claim 6: The method of Claim 1, wherein the neural feedback information derived from the neural feedback is used to update an existing knowledge neuron in the repository of knowledge neurons
Claim 7. The method of Claim 1, wherein the prediction mapping is used to generate a new knowledge data unit to be stored in the knowledge data repository.
Claim 7: The method of Claim 1, wherein the neural feedback information derived from the neural feedback is used to update an existing knowledge neuron in the repository of knowledge neurons
Claims 8-14 recite a non-transitory CRM to perform the methods of Claims 1-7. Claims 15-20 recite an apparatus to perform the methods of Claims 1-3 and 5-7.
The reference patent has corresponding CRM and apparatus comprising a processor claims 8-20
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure: Sheth et al., “kHealth: Proactive Personalized Actionable Information for Better Healthcare” teaches a knowledge graph with data obtained from IoT devices to answer queries about a person’s health.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to BRIAN M SMITH whose telephone number is (469)295-9104. The examiner can normally be reached Monday - Friday, 8:00am - 4pm Pacific.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Kakali Chaki can be reached at (571) 272-3719. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/BRIAN M SMITH/Primary Examiner, Art Unit 2122