DETAILED ACTION
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 .
It is noted per the “Response to Request for Corrected Filing Receipt” mailed on 11/14/23, and the filing receipt mailed 11/15/23, that the benefit claim to 17976923 has not been recognized by the Office. It appears the benefit claim on the ADS improperly claims benefit to a provisional instead of a non-provisional application and does not identify the relationship of the applications as required per 1.78(d)(2). For Examination purposes a date of 10/30/23 is being used.
This action is in response to the original filing on 10/30/2023. Claims 1 – 25 are pending and have been considered below.
Drawings
The drawings are objected to under 37 CFR 1.83(a) because they fail to show as described in the specification. Any structural detail that is essential for a proper understanding of the disclosed invention should be shown in the drawing. MPEP § 608.02(d). Corrected drawing sheets in compliance with 37 CFR 1.121(d) are required in reply to the Office action to avoid abandonment of the application. Any amended replacement drawing sheet should include all of the figures appearing on the immediate prior version of the sheet, even if only one figure is being amended. The figure or figure number of an amended drawing should not be labeled as “amended.” If a drawing figure is to be canceled, the appropriate figure must be removed from the replacement sheet, and where necessary, the remaining figures must be renumbered and appropriate changes made to the brief description of the several views of the drawings for consistency. Additional replacement sheets may be necessary to show the renumbering of the remaining figures. Each drawing sheet submitted after the filing date of an application must be labeled in the top margin as either “Replacement Sheet” or “New Sheet” pursuant to 37 CFR 1.121(d). If the changes are not accepted by the examiner, the applicant will be notified and informed of any required corrective action in the next Office action. The objection to the drawings will not be held in abeyance.
Specification
The disclosure is objected to because of the following informalities:
Page 2, Paragraph [0006], “and identifying”, excessive spacing
Page 25, Paragraph [0091] “having red a red color”. Typo; Duplicate of color “red” or Misspelling of “read”.
Appropriate correction is required.
Claim Objections
Claim 18 is objected to because of the following informalities: missing a period “ . ” , at the end of the claim.
Appropriate correction is required.
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 – 25 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.
Claims 1 and 18 recite “receiving text data, the text data providing a textual description relating to an incident…”, and “identifying the incident based on the one or more similarity metrics.”. It is uncertain what is meant by “identifying the incident” since the information about the incident is already provided in the text data “.
For the purpose of examination and in light of the specification, Examiner will interpret the limitation as “identifying, based on the one or more similarity metrics, the at least one object involved in the incident”.”
Claims 2-17 and 19-25 are rejected under 35 USC 112(b) because of their dependencies with the parent claims.
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.
The text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action.
Claims 1 - 25 rejected under 35 U.S.C. 101 because the claimed invention is directed to a an abstract idea without significantly more. The analysis of the claims will follow the 2019 Revised Patent Subject Matter Eligibility Guidance, 84 Fed. Reg. 50 (“2019 PEG”).
Claim 1
Step 1: This claim recites “A computer-based identification... method comprising:”; therefore, it is directed to the statutory category of a process.
Step 2A Prong 1: This claim recites, inter alia:
provide an explanation of results produced thereby, These limitations recite a mentally performable process that can be done with aid of pen and paper and practically in the human mind. Wherein providing an opinion on why results were produced, similar to collecting information, analyzing it, and displaying certain results of the collection and analysis. See MPEP 2106.04(a)(2)(III)(A)
extracting a first plurality of features from the text data; These limitations recite a mentally performable process that can be done with aid of pen and paper and practically in the human mind. Wherein observing and evaluating the feature of the text data falls under the judicial exception. Similar to collecting information, analyzing it, See MPEP 2106.04(a)(2)(III)(A).
computing one or more similarity metrics between the first plurality of features and a respective second plurality of features, These limitations recite a mathematical calculation, using an algorithm for calculating one or more values to obtain an overall score between the first and second features, e.g. computing one or more similarity metrics between the first plurality of features and a respective second plurality of features,” See MPEP 2106.04(a)(2)(I)(C).
identifying the incident based on the one or more similarity metrics. These limitations recite a mentally performable process that can be done with aid of pen and paper and practically in the human mind. Wherein the task of providing judgment whether incidents are similar based on evaluations of one or more similarity metrics observed falls under the judicial exception. Thus, this claim recites a judicial exception.
Step 2A Prong 2: This judicial exception is not integrated into a practical application. The additional elements of this claim are as follows:
at a computing device having at least one machine learning algorithm operating therein, the at least one machine learning algorithm configured to: These additional elements recite mere additional instructions to implement the judicial exception, because the additional element is but merely a tool to perform an existing process. Similar to simply adding a general-purpose computer or computer components after the fact to an abstract idea. e.g. “A computer-based identification method, the method comprising: at a computing device having at least one machine learning algorithm operating therein, the at least one machine learning algorithm configured to” See MPEP 2106.05(f). Thus, applying the abstract idea on a computer does not integrate a judicial exception into a practical application.
receiving text data, the text data providing a textual description relating to an incident involving at least one object;
These additional elements merely recites an insignificant extra-solution activity of data gathering of text data similar to obtaining information about transactions using the Internet to verify credit card transactions. The text data being textual description to compare against data provided from an incident. See MPEP 2106.05(g).
the second plurality of features derived from a plurality of explainability labels produced by the at least one machine learning algorithm, the plurality of explainability labels associated with media data obtained from one or more media devices deployed at one or more locations encompassing a location of the incident; and
These additional elements recite no more than generally linking the judicial exception of mathematical calculation to the at least one machine learning algorithm providing the second plurality of features deriving from a plurality of explainability labels that are associated with media data from one or more media devices to the technological environment. These additional elements do not integrate the judicial exception into practical application. See MPEP 2106.05(h) Thus, the way in which the additional elements use or interact with the judicial exception when analyzed with this claim as a whole do not integrate the judicial exception into a practical application.
Step 2B: The additional elements from Step 2A Prong 2 include insignificant extra-solution activities of data gathering recited by “receiving text data, the text data providing a textual description relating to an incident involving at least one object;”. This is well understood, routine, and conventional activity similar to extracting data from a physical document as described in MPEP 2106.05(d)(II)(v). Additional elements further include linking the judicial exception a particular field of use and invoking a generic computer as a tool to apply the judicial exception. Thus, the additional elements, viewed individually or in combination, do not provide an inventive concept or otherwise amount to significantly more than the abstract idea itself. See MPEP 2106.05.
Claim 2
Step 1: a process, as in claim 1.
Step 2A Prong 1: This claim recites the same abstract ideas as in claim 1 as the judicial exception.
Step 2A Prong 2: The judicial exception is not integrated into a practical application. The additional elements of the claim are as follows:
wherein receiving the text data comprises receiving one or more witness statements, the one or more witness statements providing information about at least one of a type of the incident, at least one vehicle involved in the incident, a direction of travel of the at least one vehicle, at least one person involved in the incident, and a physical environment within which the incident occurred.: These additional elements merely recites an insignificant extra-solution activity of data gathering of text data and selecting a particular data source based on similar types of information in the text data. See MPEP 2106.05(g). Thus, the way in which the additional elements use or interact with the judicial exception when analyzed with this claim as a whole do not integrate the judicial exception into a practical application.
Step 2B: The insignificant extra solution activity of mere data gather and selecting a particular data source of “wherein receiving the text data comprises receiving one or more witness statement the one or more witness statements providing information about at least one of a type of the incident, at least one vehicle involved in the incident, a direction of travel of the at least one vehicle, at least one person involved in the incident, and a physical environment within which the incident occurred.” this is well understood routine and conventional activity similar to electronic recordkeeping and sorting information as described in MPEP 2106.05(d)(II). Thus, the additional elements, viewed individually or in combination, do not provide an inventive concept or otherwise amount to significantly more than the abstract idea itself. See MPEP 2106.05.
Claim 3
Step 1: a process, as in claim 2.
Step 2A Prong 1: This claim recites the same abstract ideas as in claim 2 as the judicial exception.
Step 2A Prong 2: The judicial exception is not integrated into a practical application. The additional elements of the claim are as follows:
wherein receiving the text data comprises receiving information about at least one of physical characteristics and a physical appearance of the at least one person.: These additional elements merely recite insignificant extra-solution activity of data gathering of text data and selecting a particular data source based on similar types of information in the text data. See MPEP 2106.05(g). Thus, the way in which the additional elements use or interact with the judicial exception when analyzed with this claim as a whole do not integrate the judicial exception into a practical application.
Step 2B: The additional elements from Step 2A Prong 2 include insignificant extra-solution activities of data gathering and selecting a particular data source recited by “wherein receiving the text data comprises receiving information about at least one of physical characteristics and a physical appearance of the at least one person.” This is well understood routine and conventional activity similar to electronic recordkeeping and sorting information as described in MPEP 2106.05(d)(II). Thus, the additional elements, viewed individually or in combination, do not provide an inventive concept or otherwise amount to significantly more than the abstract idea itself. See MPEP 2106.05.
Claim 4
Step 1: a process, as in claim 1.
Step 2A Prong 1: This claim recites the same abstract ideas as in claim 1 as the judicial exception.
Step 2A Prong 2: The judicial exception is not integrated into a practical application. The additional elements of the claim are as follows:
wherein the media data comprises a plurality of images captured by one or more cameras.
These additional elements recite no more than generally linking the judicial exception, the abstract idea of mathematical calculation, to the media data comprising a plurality of images from one or more cameras as a field of use. These additional elements do not integrate judicial exception into practical application, see MPEP 2106.05(h). Thus, the way in which the additional elements use or interact with the judicial exception when analyzed with this claim as a whole do not integrate the judicial exception into a practical application.
Step 2B: The additional elements from Step 2A Prong 2 do not contain significantly more than the judicial exception. Thus, the additional elements, viewed individually or in combination, do not provide an inventive concept or otherwise amount to significantly more than the abstract idea itself. See MPEP 2106.05.
Claim 5
Step 1: a process, as in claim 4.
Step 2A Prong 1: This claim recites the same abstract ideas as in claim 4 as the judicial exception.
Step 2A Prong 2: The judicial exception is not integrated into a practical application. The additional elements of the claim are as follows:
wherein each of the plurality of images has associated therewith metadata comprising one or more vehicle characteristics.
These additional elements recite no more than generally linking the judicial exception, the abstract idea of mathematical calculation, to the plurality of images associated therewith metadata comprising one or more vehicle characteristics to a particular field of use. See MPEP2106.05(h) Thus, the way in which the additional elements use or interact with the judicial exception when analyzed with this claim as a whole do not integrate the judicial exception into a practical application.
Step 2B: The additional elements from Step 2A Prong 2 do not contain significantly more than the judicial exception. Thus, the additional elements, viewed individually or in combination, do not provide an inventive concept or otherwise amount to significantly more than the abstract idea itself. See MPEP 2106.05.
Claim 6
Step 1: a process, as in claim 1.
Step 2A Prong 1: This claim recites the same abstract ideas as in claim 1 as the judicial exception.
Step 2A Prong 2: The Judicial Exception is not integrated into a practical application. The additional elements of the claim are as follows:
wherein the media data comprises video footage captured by one or more video cameras.
These additional elements recite no more than generally linking the judicial exception of mathematical calculation to the media data comprising video footage captured to a particular field of use, the one or more video cameras, see MPEP2106.05(h). Thus, the way in which the additional elements use or interact with the judicial exception when analyzed with this claim as a whole do not integrate the judicial exception into a practical application.
Step 2B: The additional elements from Step 2A Prong 2 do not contain significantly more than the judicial exception. Thus, the additional elements, viewed individually or in combination, do not provide an inventive concept or otherwise amount to significantly more than the abstract idea itself. See MPEP 2106.05.
Claim 7
Step 1: a process, as in claim 6.
Step 2A Prong 1: This claim recites the same abstract ideas as in claim 6 as the judicial exception.
Step 2A Prong 2 The judicial exception is not integrated into a practical application. The additional elements of the claim are as follows:
wherein the video footage has metadata associated therewith, the metadata indicative of occurrence, at the one or more monitored locations, of at least one event recorded by the one or more video cameras. These additional elements recite no more than generally linking the judicial exception, the abstract idea of mathematical calculation to the video footage having metadata associated with the indicative of occurrence at the one or more monitored locations of the at least one recorded event by the one or more video camera, the particular field of use. MPEP2106.05(h) Thus, the way in which the additional elements use or interact with the judicial exception when analyzed with this claim as a whole do not integrate the judicial exception into a practical application.
Step 2B: The additional elements from Step 2A Prong 2 do not contain significantly more than the judicial exception. Thus, the additional elements, viewed individually or in combination, do not provide an inventive concept or otherwise amount to significantly more than the abstract idea itself. See MPEP 2106.05.
Claim 8
Step 1: a process, as in claim 1.
Step 2A Prong 1: This claim recites the same abstract ideas as in claim 1 as the judicial exception.
Step 2A Prong 2: The judicial exception is not integrated into a practical application. The additional elements of the claim are as follows:
wherein the text data provides a textual description of at least one vehicle involved in the incident, and the media data depicts one or more vehicles and/or license plates.: These additional elements merely recite insignificant extra-solution activity of data gathering of data and selecting a particular data source based on similar types of information in the text data. See MPEP 2106.05(g). Thus, the way in which the additional elements use or interact with the judicial exception when analyzed with this claim as a whole do not integrate the judicial exception into a practical application.
Step 2B: The additional elements from Step 2A Prong 2 include insignificant extra-solution activities of data gathering and selecting a particular data source recited by “wherein the text data provides a textual description of at least one vehicle involved in the incident, and the media data depicts one or more vehicles and/or license plates.” This is well understood routine and conventional activity similar to electronic recordkeeping and extracting data from a physical document as described in MPEP 2106.05(d)(II). Thus, the additional elements, viewed individually or in combination, do not provide an inventive concept or otherwise amount to significantly more than the abstract idea itself. See MPEP 2106.05.
Claim 9
Step 1: a process, as in claim 8.
Step 2A Prong 1: This claim recites the same abstract ideas as in claim 8 as the judicial exception.
Step 2A Prong 2: The judicial exception is not integrated into a practical application. The additional elements of the claim are as follows:
wherein the media data is retrieved from a plurality of event occurrence records stored in at least one database and has associated therewith the plurality of explainability labels indicative of an explanation of at least one categorization of the one or more vehicles produced by the at least one machine learning algorithm.: These additional elements merely recite insignificant extra-solution activity of data gathering of data and selecting a particular data source based on information for collection, and analysis. See MPEP 2106.05(g). Thus, the way in which the additional elements use or interact with the judicial exception when analyzed with this claim as a whole do not integrate the judicial exception into a practical application.
Step 2B: The additional elements from Step 2A Prong 2 include insignificant extra-solution activities of data gathering and selecting a particular data source recited by “wherein the media data is retrieved from a plurality of event occurrence records stored in at least one database and has associated therewith the plurality of explainability labels indicative of an explanation of at least one categorization of the one or more vehicles produced by the at least one machine learning algorithm.” This is well understood routine and conventional activity similar to electronic recordkeeping and sorting information as described in MPEP 2106.05(d)(II). Thus, the additional elements, viewed individually or in combination, do not provide an inventive concept or otherwise amount to significantly more than the abstract idea itself. See MPEP 2106.05.
Claim 10
Step 1: a process, as in claim 9.
Step 2A Prong 1: This claim recites the same abstract ideas as in claim 9 as the judicial exception.
Step 2A Prong 2: The Judicial Exception is not integrated into a practical application. The additional elements of the claim are as follows:
wherein the at least one categorization produced by the at least one machine learning algorithm comprises a make and/or a model of the one or more vehicles. These additional elements recite no more than generally linking a judicial exception, the mental process to at least one categorization produced by the at least one machine learning algorithm to a particular field of use. The categorization produced by the machine learning algorithm, wherein comprises a make and/or a model of the one or more vehicles. See MPEP2106.05(h) Thus, the way in which the additional elements use or interact with the judicial exception when analyzed with this claim as a whole do not integrate the judicial exception into a practical application.
Step 2B: The additional elements from Step 2A Prong 2 do not contain significantly more than the judicial exception. Thus, the additional elements, viewed individually or in combination, do not provide an inventive concept or otherwise amount to significantly more than the abstract idea itself. See MPEP 2106.05.
Claim 11
Step 1: a process, as in claim 10.
Step 2A Prong 1: This claim recites, inter alia:
wherein identifying the incident comprises identifying, based on the one or more similarity metrics, a given one of the plurality of event occurrence records relating to the incident.
These limitations recite a mentally performable process that can be done with aid of pen and paper and practically in the human mind. Wherein the task of providing judgment whether an incident is similar is based on the evaluation of the one or more similarity metrics and the plurality of event occurrence records relating to the incident observed, see MPEP 2106.04(a)(2)(III). Thus, this claim recites a judicial exception.
Step 2A Prong 2 & Step 2B: There are no additional elements recited so this claim does not provide a practical application and is not considered to be significantly more. As such, this claim is patent ineligible.
Claim 12
Step 1: a process, as in claim 11.
Step 2A Prong 1: This claim recites, inter alia:
wherein identifying the incident comprises identifying at least one of the make and the model of the at least one vehicle involved in the incident using the given one of the plurality of event occurrence records.
These limitations recite a mentally performable process that can be done with aid of pen and paper and practically in the human mind. The act of making judgment and evaluating the at least one vehicle’s “make”, and “model” involved in the incident from the plurality of event occurrence records falls under the judicial exception, see MPEP 2106.04(a)(2)(III) Thus, this claim recites a judicial exception.
Step 2A Prong 2 & Step 2B: There are no additional elements recited so this claim does not provide a practical application and is not considered to be significantly more. As such, this claim is patent ineligible.
Claim 13
Step 1: a process, as in claim 1.
Step 2A Prong 1: This claim recites the same abstract ideas as in claim 1 as the judicial exception.
Step 2A Prong 2 The judicial exception is not integrated into a practical application. The additional elements of the claim are as follows:
wherein extracting the first plurality of features comprises applying at least one Natural Language Processing technique to the text data to extract one or more words from the text data. These additional elements recite mere instructions to apply the judicial exception and are only the idea of a solution or outcome. e.g. “wherein extracting the first plurality of features comprises applying at least one Natural Language Processing technique to the text data to extract one or more words from the text data.” see MPEP2106.05(f) Thus, the way in which the additional elements use or interact with the judicial exception when analyzed with this claim as a whole do not integrate the judicial exception into a practical application.
Step 2B: The additional elements from Step 2A Prong 2 do not contain significantly more than the judicial exception. Thus, the additional elements, viewed individually or in combination, do not provide an inventive concept or otherwise amount to significantly more than the abstract idea itself. See MPEP 2106.05.
Claim 14
Step 1: a process, as in claim 13.
Step 2A Prong 1: This claim recites the same abstract ideas as in claim 1 as the judicial exception.
Step 2A Prong 2: The judicial exception is not integrated into a practical application. The additional elements of the claim are as follows:
wherein computing the one or more similarity metrics comprises computing at least one score indicative of a similarity between the one or more words and the second plurality of features. These additional elements are mere additional instructions to implement the judicial exception because the additional elements only recite the idea of a solution or outcome. E.g. “wherein computing the one or more similarity metrics comprises computing at least one score indicative of a similarity between the one or more words and the second plurality of features.” See MPEP 2106.05(f). These additional elements when analyzed with this claim as a whole do not integrate the judicial exception into a practical application nor significantly amount to more than the judicial exception.
Step 2B: The additional elements from Step 2A Prong 2 do not contain significantly more than the judicial exception. Thus, the additional elements, viewed individually or in combination, do not provide an inventive concept or otherwise amount to significantly more than the abstract idea itself. See MPEP 2106.05.
Claim 15
Step 1: a process, as in claim 1.
Step 2A Prong 1: This claim recites, inter alia:
further comprising assigning a ranking to the one or more similarity metrics, the incident identified based on the ranking.
These limitations recite a mentally performable process of an evaluation and judgement of the one or more similarity metrics to provide a ranking for the identified incident, see MPEP 2106.04(a)(2)(III). Thus, this claim recites a judicial exception.
Step 2A Prong 2 & Step 2B: There are no additional elements recited so this claim does not provide a practical application and is not considered to be significantly more. As such, this claim is patent ineligible.
Claim 16
Step 1: a process, as in claim 15.
Step 2A Prong 1: This claim recites, inter alia:
This limitation furthers the judicial exception of mental process in claim 15.
further comprising outputting the ranking.
These limitations recite a mentally performable process that can be done with aid of pen and paper and practically in the human mind. After the evaluation of Claim 15, the ranking is output. Thus, this claim recites a judicial exception.
Step 2A Prong 2 & Step 2B: There are no additional elements recited so this claim does not provide a practical application and is not considered to be significantly more. As such, this claim is patent ineligible.
Claim 17
Step 1: a process, as in claim 1.
Step 2A Prong 1: This claim recites, inter alia:
further wherein the at least one object is identified based on the one or more similarity metrics.
These limitations recite a mentally performable process that can be done with aid of pen and paper and practically in the human mind. Wherein at least one object is to be identified based on an evaluation and observation of the one or more similarity metrics.
Step 2A Prong 2: The Judicial Exception is not integrated into a practical application. The additional elements of the claim are as follows:
wherein the text data provides the textual description relating to the incident involving at least one object: These additional elements recite no more than generally linking a judicial exception, the mental process to the incident involving at least one object provided by the textual description to a particular field of use, see MPEP2106.05(h). Thus, the way in which the additional elements use or interact with the judicial exception when analyzed with this claim as a whole do not integrate the judicial exception into a practical application.
Step 2B: The additional elements from Step 2A Prong 2 do not contain significantly more than the judicial exception. Thus, the additional elements, viewed individually or in combination, do not provide an inventive concept or otherwise amount to significantly more than the abstract idea itself. See MPEP 2106.05.
Claims 18 - 25
Step 1: These claims are directed to “A computer-implemented identification system, the system comprising : a processing unit having… a non-transitory computer-readable medium having stored thereon program instructions executable by the processing unit for:”; therefore, these claims are directed to the statutory category of machines.
Step 2A Prong 1:
These claims recite the same abstract ideas as in claims 1, 2, 8-12, and 13&14, respectively, as the judicial exception.
Step 2A Prong 2: The judicial exception recited in this claim is not integrated into a practical application.
These claims recite substantially the same additional elements as in claims 1, 2, 8-12, and 13&14, respectively.
The only differences between these claims and claims 1, 2, 8-12, and 13&14, are that these claims are directed to a “A computer-implemented identification system, the system comprising: a processing unit having… a non-transitory computer-readable medium having stored thereon program instructions executable by the processing unit for:” However, mere recitation that a judicial exception is to be performed using generic computer equipment in their ordinary capacity, i.e. a computer-implemented identification system, the system comprising: a processing unit, a non-transitory computer readable medium having stored thereon instructions for causing a processing circuitry to execute a process, cannot meaningfully integrate the judicial exception into a practical application. See MPEP 2106.05(f). With that exception, the analysis at this step mirrors that of claims 1, 2, 8-12, and 13&14.
Step 2B: The additional elements from Step 2A Prong 2 of this claim does not contain significantly more than the judicial exception. The only differences between these claims and claims 1, 2, 8-12, and 13&14, are that these claims are directed to a “A computer-implemented identification system, the system comprising: a processing unit having… a non-transitory computer-readable medium having stored thereon program instructions executable by the processing unit for:” However, mere recitation that a judicial exception is to be performed using generic computer equipment in their ordinary capacity, i.e. a computer-implemented identification system, the system comprising: a processing unit, a non-transitory computer readable medium having stored thereon instructions for causing a processing circuitry to execute a process, cannot amount to significantly more than the judicial exception. See MPEP 2106.05(f). With that exception, the analysis at this step mirrors that of claims 1, 2, 8-12, and 13&14.
Claim Rejections - 35 USC § 102
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)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1, 4, 6, 13 - 18, and 25 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Gong et al. (US 20240028631 A1) (hereinafter Gong).
Regarding claim 1, Gong teaches A computer-based identification method, the method comprising:
at a computing device having at least one machine learning algorithm operating therein, the at least one machine learning algorithm configured to provide an explanation of results produced thereby ([0010], “a computer-implemented method of performing a semantic textual similarity search between a target document and a set of source documents… identifying and classifying, by an entity extractor,… measuring the similarities of the target document embedding with all of the source documents embedding; and computing, by an explainability generator,”. [0034], “the explainability generator 128 is configured to provide explanations 130 (i.e. to explain the reason(s)) why those documents are believed by the system to be similar to the respective input document”),
receiving text data, the text data providing a textual description relating to an incident involving at least one object ([0024], “the input document d contains both structured and unstructured data, the process proceeds with a textual feature selection module 104. More specifically, after receiving the input document d from the agent.” [0075], “To do so, the entity extractor may first extract the important entities from the text.” Examiners Remarks: Wherein under BRI the at least one object would be one of the important entities with involvement to an incident);
extracting a first plurality of features from the text data ([0024], “the input document d is forwarded to the textual feature selection module 104 that is configured to execute a feature selection procedure to distinguish the textual features and the non-textual features”);
computing one or more similarity metrics between the first plurality of features and a respective second plurality of features, ([0028], “the semantic textual similarity (STS) model 114 , [0032], “According to an embodiment, this task may be performed by measuring the similarities between the document embedding d* and all the source documents embedding S*, thereby generating a set 126 of similar documents <S*, d*>.” Examiners Remarks: Wherein the plurality of the textual features are compared to find a similar between the target document and sources documents.) the second plurality of features derived from a plurality of explainability labels produced by the at least one machine learning algorithm, the plurality of explainability labels associated with media data obtained from one or more media devices deployed at one or more locations encompassing a location of the incident ([0032], “The source documents embedding S* may be encoded based on source documents S in the same way as the document d* and as described above (i.e. including the steps of textual features selection, entity identification, word embedding and feature aggregation)” [0075], “the similarity estimator may return the similar incidents by evaluating the similarity of the input incident with all the recorded incidents. As a result, by referencing the reaction of the similar accident(s) found by the system, an associated dispatching system can quickly adjust any remote-controlled road devices, for example, digital speed signs, electronic road blocker and cameras (e.g., to take photos/videos of the accident scene as soon as possible).” Wherein source documents go through the same process as the input document (textual data) and are a second plurality of features derived from a database of documents. Furthermore, [0024], “the input document d is forwarded to the textual feature selection module 104 that is configured to execute a feature selection procedure to distinguish the textual features and the non-textual features” Examiners Remarks: Wherein from the database of documents, the non-textual features are explainability labels associated with the media data of the photos and videos taken from one or more locations.); and
identifying the incident based on the one or more similarity metrics. ([0032], “the document similarity may be estimated by a similarity estimator 122. To this end, given the document embedding d*, the similarity estimator 122 may be configured to search for similar documents in a set 124 of possible documents S*. According to an embodiment, this task may be performed by measuring the similarities between the document embedding d* and all the source documents embedding S*, thereby generating a set 126 of similar documents <S*, d*>.”).
Regarding claim 4, Gong teaches the method of claim 1, and wherein the media data comprises a plurality of images captured by one or more cameras, ([0075], “As a result, by referencing the reaction of the similar accident(s) found by the system, an associated dispatching system can quickly adjust any remote-controlled road devices, for example, digital speed signs, electronic road blocker and cameras (e.g., to take photos/videos of the accident scene as soon as possible).” [0024], “the input document d is forwarded to the textual feature selection module 104 that is configured to execute a feature selection procedure to distinguish the textual features and the non-textual features” Examiners Remarks: Under BRI the media data is associated with the non-textual features of the source document database, and the documents contains a plurality of images (photos) taken by the one or more cameras. )
Regarding claim 6, Gong teaches the method of claim 1, wherein the media data comprises video footage captured by one or more video cameras. ([0075], “As a result, by referencing the reaction of the similar accident(s) found by the system, an associated dispatching system can quickly adjust any remote-controlled road devices, for example, digital speed signs, electronic road blocker and cameras (e.g., to take photos/videos of the accident scene as soon as possible).” [0024], “the input document d is forwarded to the textual feature selection module 104 that is configured to execute a feature selection procedure to distinguish the textual features and the non-textual features” Examiners Remarks: Under BRI the media data is associated with the non-textual features of the source document database, and the documents contains a plurality of video footage taken by the one or more cameras.)
Regarding claim 13, Gong discloses the method of claim 1, wherein extracting the first plurality of features comprises applying at least one Natural Language Processing technique to the text data to extract one or more words from the text data. ([0024], “the process proceeds with a textual feature selection module [extract one or more words from the text data] 104. More specifically, after receiving the input document d from the agent 102, the input document d is forwarded to the textual feature selection module 104 that is configured to execute a feature selection procedure to distinguish the textual features and the non-textual features. As shown at S2 and S3, respectively, the module 104 may be configured to forward only the textual features to the next natural language processing stages”. Furthermore see Fig. 1. Examiners remarks: The feature selection module extracts the one or more words from the text data [0015], “ According to an embodiment of the invention, the similarity search exploits both the textual features and the name entity information… By relying on both parts and by aggregating the entity labels with the original textual tokens, the context is provided, i.e. the word embedding encodes the semantic meaning. This results in a performance improvement since scenarios are addressed in which two equal words with different entity types have the same embedding but actually a different meaning (for example jaguar: car vs. animal). ”)
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Regarding claim 14 Gong further teaches the method of claim 13, wherein computing the one or more similarity metrics comprises computing at least one score indicative of a similarity between the one or more words and the second plurality of features. ([0018] b. Based on the token embedding generated by the semantic textual similarity model, the entity pooling strategy may produce the final word embedding of the textual data by extracting the embedding of the important entities from the token embedding and aggregating the extracted entity embedding with the sentence embedding (i.e., the sum of the token embedding). Furthermore, [0059], “computed by calculating the cosine similarity between the two embeddings [a similarity between the one or more words] of the entity ei in d1 and d2 [the second plurality of features.] Ideally, the similarity calculation should be the same as the calculation used by the similarity estimator 122 (as described in connection with FIG. 1). After this step, each partition 310, 312 has a SimScore 314 [at least one score indicative of a similarity].” Examiners remarks: Examiner is interpreting the two embeddings as mapping to a similarity between the one or more words since the two embeddings are aggregations of previous embedding which includes a similarity between the one or more words. Furthermore, the second plurality of features mapping to d2 I.e. media data)
Regarding claim 15, Gong teaches the method of claim 1, further comprising assigning a ranking to the one or more similarity metrics, the incident identified based on the ranking. ([0033], “the similarity estimator 122 may assign each source document a similarity score. Depending on the setting, the output of the similarity estimator 122 could be, for instance, the top k most similar documents [the incident identified based on the ranking] (where k is a configurable parameter), or all the source documents above a chosen similarity score or threshold. Alternatively, the source documents could just be ranked within their database according to their similarity score.” Examiner Remarks: Wherein top k most similar documents, identifies the incident by the highest ranking amongst similar incidents “source documents” )
Regarding claim 16, Gong teaches the claimed invention as claimed in the method of claim 15, further comprising outputting the ranking. ([0068], “Instantiating the similarity estimator to search the similar documents from the source documents and deciding how to return the similar documents (top-k, ranking, threshold-based)”)
Regarding claim 17, Gong further discloses the method of claim 1, wherein the text data provides the textual description relating to the incident involving at least one object, ([0024], “the input document d contains both structured and unstructured data, the process proceeds with a textual feature selection module 104. More specifically, after receiving the input document d from the agent.” [0075], “To do so, the entity extractor may first extract the important entities from the text.” Wherein, under BRI the at least one object is one of the important entities with involvement an incident) further wherein the at least one object is identified based on the one or more similarity metrics. ([0083], “Based on the identified entity types, the entity pooling strategy may then extract the entity embedding from the token embedding. Given the entity embedding of each document, the similarity estimator may then scan all the documents and compute how similar each pair of documents is with regard to the different entities of interest. ” Examiners remarks: the entities of interest the at least one object, “one of the Important entities” is identified based on the similarity estimator, which maps to the one or more similarity metrics.)
Regarding claims 18, and 25, these are system claims that corresponds to the methods of claims 1, and 13&14, respectively. Therefore, these are rejected for the same reasons as claims 1, and 13&14 above.
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 5, 7, 8 – 12, and 20 – 24 are rejected under 35 U.S.C. 103 as being unpatentable over Gong et al. (US 20240028631 A1), as applied in claims 1, 6, and 18 above, (hereinafter Gong) in view of Pitt et al. (US 20160140398 A1) (hereinafter Pitt).
Regarding claim 5, Gong teaches the claimed invention as claimed in the method of claim 4, Gong does not expressly teach wherein each of the plurality of images has associated therewith metadata comprising one or more vehicle characteristics.
However, Pitt teaches wherein each of the plurality of images has associated therewith metadata comprising one or more vehicle characteristics. (Pitt, [0033], “employ optical character recognition (OCR) to directly recognize text in the visual media and/or by recognizing text associated with landmarks, common objects or recognized faces as a form of meta-data.” Pitt, [0042],” is illustrated in FIG. 7 in a still frame 450, text from exterior surfaces of vehicles, including text from either make (e.g., a manufacturer) and/or model designations from a vehicle license plate” Pitt further depicted this limitation is FIG. 14)
Because Gong and Pitt are analogous art and within the same field of endeavor, specifically computer-implemented systems that extract and matches information from an input to a respective database of similar information. Accordingly, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to combine Pitt’s, contextual information recognition to capture visual media (i.e. the plurality of images) metadata comprising of vehicle characteristics with Gong’s, textual similarity search associated with the media data of the plurality of images. This modification would have been motivated by the desire to improve response time of duly authorized users and improve situational awareness in situations. (Pitt, [0032])
Regarding claim 7, Gong teaches the claimed invention as claimed in the method of claim 6. Gong does not expressly teach wherein the video footage has metadata associated therewith, the metadata indicative of occurrence, at the one or more monitored locations, of at least one event recorded by the one or more video cameras.
However, Pitt teaches wherein the video footage has metadata associated therewith, the metadata indicative of occurrence, at the one or more monitored locations, (Pitt, [0027], “The media source 54 could include, for example, a device configured to capture visual media, such as a camera. The media source 54 can be implemented, for example, on a traffic camera, a security camera, a satellite camera, a hand-held camera (e.g., a smart phone or other device), etc. The media source 54 can be configured to capture still frames and/or video (e.g., successive still frames).”) of at least one event recorded by the one or more video cameras. (Pitt, [0033], “The recognizer 60 can, for example, employ optical character recognition (OCR) to directly recognize text in the visual media and/or by recognizing text associated with landmarks, common objects or recognized faces as a form of meta-data”. Furthermore, Pitt, [0034], “Similarly, names of a street extracted from street signs in the visual media can be employed by the information finder 62 to query a map service (e.g., GOOGLE MAPS®) to determine a location of the scene captured by the visual media.”)
Because Gong and Pitt are analogous art and within the same field of endeavor, specifically computer-implemented systems that extract and matches information from an input to a respective database of similar information. Accordingly, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to combine Pitt’s, contextual information recognition of visual media with Gong’s, textual similarity search system. This modification would have been motivated by the desire to improve response time of duly authorized users and improve situational awareness in situations. (Pitt, [0032])
Regarding claim 8, Gong teaches the claimed invention as claimed in the method of claim 1, wherein the text data provides a textual description of at least one vehicle involved in the incident, (Gong, [0075], “the entity extractor may first extract the important entities from the text, such as number/type of vehicles involved in the accident”). Gong does not expressly teach, and the media data depicts one or more vehicles and/or license plates.
However, Pitt does teach, and the media data depicts one or more vehicles and/or license plates. (Pitt, [0042],“as is illustrated in FIG. 7 in a still frame 450, text from exterior surfaces of vehicles, including text from either make (e.g., a manufacturer) and/or model designations from a vehicle license plate has a high contrast between the text and the background of the text.”)
Because Gong and Pitt are analogous art and within the same field of endeavor, specifically computer-implemented systems that extract and matches information from an input to a respective database of similar information. Accordingly, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to combine Pitt’s, contextual information recognition of visual media with Gong’s, textual similarity search system. This modification would have been motivated by the desire to improve response time of duly authorized users and improve situational awareness in situations. (Pitt, [0032])
Regarding claim 9, the combination Gong and Pitt teaches the claimed invention as claimed in the method of claim 8, wherein the media data is retrieved from a plurality of event occurrence records stored in at least one database and has associated( Gong, [0075], “As a result, by referencing the reaction of the similar accident(s) found by the system, an associated dispatching system can quickly adjust any remote-controlled road devices, for example, digital speed signs, electronic road blocker and cameras (e.g., to take photos/videos of the accident scene as soon as possible).” Gong, [0010], “searching, by a similarity estimator, for similar documents by measuring the similarities of the target document embedding with all of the source documents embedding” Under BRI the media data (i.e. videos and photo obtained by any remote-controlled road device)is associated with the source document and the source document is a database of all incidents (a plurality of event occurrence records) to be compared to a target document (a new event occurrence)).
therewith the plurality of explainability labels indicative of an explanation of at least one categorization of the one or more vehicles produced by the at least one machine learning algorithm.
(Gong, [0075], “Given the textual incident report and the entities of interest, embodiments of the invention provide a system that can automatically detect the similar incidents. To do so, the entity extractor may first extract the important entities from the text, such as number/type of vehicles involved in the accident,” Furthermore, Gong, [0025], “the entity extractor 106 could be implemented in form of a neural network based language model.”)
Regarding claim 10, the combination Gong and Pitt teaches the claimed invention as claimed in the method of claim 9, wherein the at least one categorization produced by the at least one machine learning algorithm comprises a make and/or a model of the one or more vehicles. (Pitt, [0042], “The recognizer 310 can analyze a still frame of visual media to extract information. Additionally, as is illustrated in FIG. 7 in a still frame 450, text from exterior surfaces of vehicles, including text from either make (e.g., a manufacturer) and/or model designations from a vehicle license plate”.)
Regarding claim 11, the combination of Gong and Pitt teaches the method of claim 10, wherein identifying the incident comprises identifying, based on the one or more similarity metrics, a given one of the plurality of event occurrence records relating to the incident. (Gong, [0032], “ given the document embedding d*, the similarity estimator 122 may be configured to search for similar documents in a set 124 of possible documents S*. According to an embodiment, this task may be performed by measuring the similarities between the document embedding d* and all the source documents embedding S*”)
Regarding claim 12, the combination of Gong and Pitt teaches the claimed invention as claimed in the method of claim 11, wherein identifying the incident comprises identifying at least one of the make and the model of the at least one vehicle involved in the incident . (Pitt, [0042], “The recognizer 310 can analyze a still frame of visual media to extract information. Additionally, as is illustrated in FIG. 7 in a still frame 450, text from exterior surfaces of vehicles, including text from either make (e.g., a manufacturer) and/or model designations from a vehicle license plate”. Furthermore, Pitt [0034], FIG. 4 illustrates a still frame 200 that depicts an example of such an emergency situation, which still frame 200 illustrates a collision between an automobile and a motorcycle. )using the given one of the plurality of event occurrence records. (Gong, [0075], “Given the textual incident report and the entities of interest, embodiments of the invention provide a system that can automatically detect the similar incidents.”)
Regarding claims 20 – 24 , These are system claims that corresponds to the methods of claims 8 – 12, respectively. Therefore, There are rejected for the same reason as claims 8 – 12 above.
Claims 2 and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Gong et al. (US 20240028631 A1) (hereinafter Gong) as applied to claims 1 and 18 above, and further in view of Child. (US 20230073717 A1) (hereinafter Child).
Regarding claim 2, Gong teaches the claimed invention as claimed in the method of claim 1.
Gong further teaches at least one of a type of the incident, at least one vehicle involved in the incident ( Gong, [0075], “Generally, duplicate incident detection aims at helping a police system to quickly react to traffic accidents by searching the similar incident type [at least one of a type of the incident] and referencing the disposition of the similar accidents. This will save time and resources and improve the effectiveness of the dispatching. Given the textual incident report and the entities of interest, embodiments of the invention provide a system that can automatically detect the similar incidents. To do so, the entity extractor may first extract the important entities from the text, such as number/type of vehicles involved in the accident, location and the injured person(s)”)
Gong does not expressly teach, wherein receiving the text data comprises receiving one or more witness statements, the one or more witness statements providing information about, a direction of travel of the at least one vehicle, at least one person involved in the incident, and a physical environment within which the incident occurred.
However, Child teaches, wherein receiving the text data comprises receiving one or more witness statements, the one or more witness statements providing information (Child, [0026], “A user can access the user interface and provide various inputs into the user interface. The inputs may include one or more of time, location, license plate numbers, partial license plate numbers, and/or data related to a witness statement or the actual witness statement.”) about, a direction of travel of the at least one vehicle, at least one person involved in the incident, and a physical environment within which the incident occurred. (Child, [0040], “associated with the target of interest at a series of collection stations positioned at selected locations throughout a geographic area, and plotting movement of the target of interest throughout the geographic area. In some embodiments, the identifying information for known targets of interest includes vehicle identifiers” furthermore Child, [0045], “ In embodiments of the method, tracking the one or more targets of interest comprises determining a location of a selected target, an association of the selected target to one or more persons, association of the target to one or more locations, travel patterns of the selected target, or combinations thereof.” Examiner Remarks: the target is being interpreted a vehicle, person of interest involved with in the incident occurrence. )
Because Gong and Child are analogous art and within the same field of endeavor, specifically computer-implemented systems of identification for incidents matching and recognition. Accordingly, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to combine Child’s, visual identification surveillance system with Gong’s, textual similarity search system. This modification would have been motivated by the desire to provide for more precise, reliable, and/or consistent identification or tracking of vehicles, as well as persons associated with, and not associated with, vehicles. (Child, [0005])
Regarding claim 19, this claim is system claim that corresponds to the methods of claim 2, respectively. Therefore, claim 19 is rejected for the same reason as claim 2 above
Claims 3 is rejected under 35 U.S.C. 103 as being unpatentable over Gong et al. (US 20240028631 A1) (hereinafter Gong), and further in view of Child. (US 20230073717 A1) (hereinafter Child) as applied to claim 2 above, and in further view of Pitt et al. (US 20160140398 A1) (hereinafter Pitt)
Regarding claim 3, the combination of Gong and Child teaches the claimed invention as claimed in the method of claim 2. The combination of Gong and Child does not expressly teach wherein receiving the text data comprises receiving information about at least one of physical characteristics and a physical appearance of the at least one person.
However, Pitt teaches wherein receiving the text data comprises receiving information about at least one of physical characteristics and a physical appearance of the at least one person. (Pitt, [0043], “the text is extracted (derived) from meta-data associated with… faces” Furthermore, Pitt, [0063],” The face recognizer 328 can be employed to implement a facial recognition algorithm on the visual media to determine a possible identity for an individual (e.g., a person)”.)
Because the combination of Gong, Child and Pitt are analogous art and within the same field of endeavor, specifically computer-implemented systems of identification that extracts and matches information from an input to a respective database of similar information. Accordingly, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to combine Pitt’s, contextual information recognition to capture physical characteristics and physical appearance with the combination of Gong and Child’s, identification system. This modification would have been motivated by the desire to improve response time of duly authorized users for improved situational awareness in situations. (Pitt, [0032])
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Vijay, (US 20170243112 A1) discloses identification of context/semantic related information of an incident based on unsupervised deep learning of past incidents of interest.
Boris, (WO 2020010459 A1) discloses systems and methods for determining similarities between representations that describe a given item and those that describe potential imitations of that given item as compared to a certain baseline item or frame of reference.
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/S.B./Examiner, Art Unit 2143 /JENNIFER N WELCH/Supervisory Patent Examiner, Art Unit 2143
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