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 .
Status of Application
This office action is in response to the most recent filings filed by applicants on 07/15/26.
Claims 1, and 7-8 are amended
No claims are cancelled
Claims 9-12 are newly added
Claims 1-12 are pending
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 1-12 is/are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., an abstract idea) without significantly more.
Step One - First, pursuant to step 1 in the January 2019 Guidance on 84 Fed. Reg. 53, the claims 1-6 and 9-12 is/are directed to a device/apparatus which is a statutory category.
Step One - First, pursuant to step 1 in the January 2019 Guidance on 84 Fed. Reg. 53, the claim 7 is/are directed to a method which is a statutory category.
Step One - First, pursuant to step 1 in the January 2019 Guidance on 84 Fed. Reg. 53, the claim 8 is/are directed to a system which is a statutory category.
Step 2A Prong 1: Identify the Abstract Idea(s)
The Alice framework, steps 2A-Prong One (part 1 of Mayo Test), here, the claims are analyzed to determine if the claims are directed to a judicial exception. MPEP 2106.04(a). In determining whether the claims are directed to a judicial exception, the claims are analyzed to evaluate whether the claims recite a judicial exception (Prong One of Step 2A), and whether the claims recite additional elements that integrate the judicial exception into a practical application (Prong Two of Step 2A). See 2019 Revised Patent Subject Matter Eligibility Guidance (“PEG” 2019 Revised Patent Subject Matter Eligibility Guidance, 84 Fed. Reg. 50-57 (Jan. 7, 2019)).
Under the 2019 PEG, Step 2A under which a claim is not “directed to” a judicial exception unless the claim satisfies a two-prong inquiry. Further, particular groupings of abstract ideas are consistent with judicial precedent and are based on an extraction and synthesis of the key concepts identified by the courts as being abstract.
Independent claims 1, 7 and 8, with respect to the Step 2A, Prong One, when “taken as a whole” the claims as drafted, and given their broadest reasonable interpretation, fall within the Abstract idea grouping of “certain methods of organizing human activity” (business relations; relationships or interactions between people). For instance, independent Method Claim 7 is directed to an abstract idea, as evidenced by claim limitations “an acquisition step of acquiring user information about a user who has visited at least one of a plurality of places included in a predetermined area; an estimation step of using a generative model trained to output a response to an input question to: extract, for each place included in the predetermined area, characteristics of the place from area information, movement or behavior associated with the user who has visited the place, and data about an interest of the user, cluster places having similar characteristics into a cluster, and output, in accordance with an output format, the target area, a title of the cluster, and a description of the cluster as the characteristic of the extracted target area, wherein the output format specifies the target area by a latitude and longitude, and wherein the cluster comprises the target area; and a display step of displaying the characteristic of the target area estimated in the estimation step as summary content including map data and a message, by performing mapping on the map data and distinguishing the mapping by shape or color to visualize a range of the target area on a map.”
The PGPub (US 2025/0322420) of the current application recites in [0027]: Next, the information processing apparatus 10 inputs the acquired data to a generative model that is trained to output an answer to an input question and estimates the characteristics of a target area. For example, the information processing apparatus 10 inputs, to the generative model, a prompt including data about the user who has visited at least one of a plurality of places included in a predetermined area and an instruction sentence of natural language indicating an instruction for estimation of a characteristic place included in the predetermined area, from the data, as the target area, and estimates the characteristic place included in the predetermined area, as the target area. Furthermore, the information processing apparatus 10 creates and estimates a summary content by using image data and a message, for the target area to be estimated. For example, the information processing apparatus 10 estimates map data, as the image data, and estimates bulleted sentences representing the characteristics of the target area, as the message. Furthermore, the information processing apparatus 10 performs mapping on the estimated map data, and distinguishes the mapping by shape or color. Then, the information processing apparatus 10 displays the estimated image data and message on a screen of the user terminal device 20. [0028] In other words, the information processing apparatus 10 reads data about the user information, data about the position information, and various data about the facility information, estimates the characteristics of the target area by using the generative model, and displays a result of the estimation on the user terminal device 20. As a result, the information processing apparatus 10 can visualize a range and characteristics having a certain meaning, on the map with the generative model by using the attributes, search behavior, hesitation, characteristic service, or the like of the user who has visited a certain place. Also see Drawings Figs. 8 and 14.
These claim limitations belong to the grouping of “certain methods of organizing human activity” because the claims are related to managing information about an area based on information gathered from users visiting a certain area and using user inputs to then estimate characteristics of the target area for one or more human entities involves organizing human activity based on the description of “certain methods of organizing human activity” provided by the courts. The court have used the phrase “Certain methods of organizing human activity” as —fundamental economic principles or practices (including hedging, insurance, mitigating risk); commercial or legal interactions (including agreements in the form of contracts; legal obligations; advertising, marketing or sales activities or behaviors; business relations); managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions).
Independent Claims 1 and 8 is/are recite substantially similar limitations to independent claim 7 and is/are rejected under 2A for similar reasons to claim 7 above.
Step 2A Prong 2: Additional Elements That Integrate the Judicial Exception into a Practical Application
With respect to the Step 2A, Prong Two - This judicial exception is not integrated into a practical application. In particular, the claim recites additional elements: “An information processing method comprising: using a generative model trained to, A non-transitory computer readable storage medium storing an information processing program for causing a computer to execute: An information processing apparatus comprising:” at a high level of generality such that it amounts to no more than: adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea, as discussed in MPEP 2106.05(f). 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 with no significantly more elements.
Thus, the additional elements do not integrate the abstract idea into practical application because they do not impose any meaningful limitations on practicing the abstract idea. As a result, claims 1, 7-8 do not provide any specifics regarding the integration into a practical application when recited in a claim with a judicial exception. See MPEP 2106.05(f).
Applicants originally submitted specification describes the computer components above at least in page/ paragraph [0024]-[0025], [0027]-[0028], [0037], [0042]. In light of the specification, it should be noted that the components discussed above did not meaningfully limit the abstract idea because they merely linked the use of the abstract idea to a particular technological environment (i.e., "implementation via computers").
The additional elements of a “using a generative model trained to”. This language merely requires execution of an algorithm that can be performed by a generic computer component and provides no detail regarding the operation of that algorithm. As such, the claim requirement amounts to mere instructions to implement the abstract idea on a computer, and, therefore, is not sufficient to make the claim patent eligible. See Alice, 573 U.S. at 226 (determining that the claim limitations “data processing system,” “communications controller,” and “data storage unit” were generic computer components that amounted to mere instructions to implement the abstract idea on a computer); October 2019 Guidance Update at 11–12 (recitation of generic computer limitations for implementing the abstract idea “would not be sufficient to demonstrate integration of a judicial exception into a practical application”). Such a generic recitation of “using a generative model trained to” is insufficient to show a practical application of the recited abstract idea. All of these additional elements are not significantly more because these, again, are merely the software and/or hardware components used to implement the abstract idea on a general-purpose computer.
Similarly dependent claims 2-6 and 9-12 are also directed to an abstract idea under 2A, first and second prong. In the present application, all of the dependent claims have been evaluated and it was found that they all inherit the deficiencies set forth with respect to the independent claims. For instance, dependent claims 2 recite “wherein the estimation unit extracts, from among places included in the predetermined area, a common place in the user information, as the target area, and estimates a characteristic of the extracted target area based on the user information about the user who has visited the target area” and dependent claims 3 recite “wherein the acquisition unit acquires, as the user information, user information about a user who has moved from a predetermined place of departure to a predetermined destination via a predetermined route, and the estimation unit extracts a characteristic place on the predetermined route as a target area, from the user information, and estimates a characteristic of the extracted target area”. Dependent claims 4 recite “wherein the acquisition unit further acquires characteristic information indicating a characteristic of each place included in the predetermined area, and the estimation unit further extracts the target area based on the characteristic information”. Dependent claims 5 recite “wherein the estimation unit estimates a content of description about the user who has visited the area, as the characteristic of the target area”. Dependent claims 6 recite “wherein the display unit displays content in which a result of the estimation by the estimation unit is associated with the target area”. Dependent claims 9 recite “wherein the estimation unit inputs, to the generative model, a prompt including the user information and an instruction sentence of natural language indicating an instruction for estimation of a characteristic place included in the predetermined area as the target area”. Dependent claims 10 recite “wherein the display unit displays the characteristic of the target area as summary content including map data and a message and performs mapping on the map data and distinguishes the mapping by shape or color”. Dependent claims 11 recite “wherein the estimation unit inputs, to the generative model, a prompt including instruction information for summarizing estimation results and instruction information for naming a title of obtained summary content, and the generative model outputs the title and the description of the cluster based on the instruction information”. Dependent claims 12 recite “wherein the acquisition unit further acquires facility information including a dwell time, opening hours, and a crowded condition for each place included in the predetermined area, and the estimation unit extracts the target area further based on the facility information”. Here, these claims offer further descriptive limitations of elements found in the independent claims which are similar to the abstract idea noted in the independent claim above. Any additional elements recited are still being recited such that it amounts to no more than: adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer or merely uses a computer as a tool to perform an abstract idea, as discussed in MPEP 2106.05(f). As a result, Examiner asserts that dependent claims, such as dependent claims 2-6 and 9-12 are also directed to the abstract idea identified above.
Step 2B: Determine Whether Any Element, Or Combination, Amount to “Significantly More” Than the Abstract Idea Itself
With respect to Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. First, the invention lacks improvements to another technology or technical field [see Alice at 2351; 2019 IEG at 55], and lacks meaningful limitations beyond generally linking the use of an abstract idea to a particular technological environment [Alice at 2360, 2019 IEG at 55], and fails to effect a transformation or reduction of a particular article to a different state or thing [2019 IEG, 55]. For the reasons articulated above, the claims recite an abstract idea that is limited to a particular field of endeavor (MPEP § 2106.05(h)) and recites insignificant extra-solution activity (MPEP § 2106.05(g)). By the factors and rationale provided above with respect to these MPEP sections, the additional elements of the claims that fail to integrate the abstract idea into a practical application also fail to amount to “significantly more” than the abstract idea.
As discussed above with respect to integration of the abstract idea into a practical application, the additional element(s) of “An information processing method comprising: using a generative model trained to, A non-transitory computer readable storage medium storing an information processing program for causing a computer to execute: An information processing apparatus comprising:” are insufficient to amount to significantly more. Applicants originally submitted specification describes the computer components above at least in page/ paragraph [0024]-[0025], [0027]-[0028], [0037], [0042]. In light of the specification, it should be noted that the components discussed above did not meaningfully limit the abstract idea because they merely linked the use of the abstract idea to a particular technological environment (i.e., "implementation via computers"). In light of the specification, it should be noted that the claim limitations discussed above are merely instructions to implement the abstract idea on a computer. See MPEP 2106.05(f). (See MPEP 2106.05(f) - Mere Instructions to Apply an Exception - “Thus, for example, claims that amount to nothing more than an instruction to apply the abstract idea using a generic computer do not render an abstract idea eligible.” Alice Corp., 134 S. Ct. at 235). Mere instructions to apply an exception using computer component cannot provide an inventive concept.). The additional elements amount to no more than a recitation of generic computer elements utilized to perform generic computer functions, such as performing repetitive calculations, Bancorp Services v. Sun Life, 687 F.3d 1266, 1278, 103 USPQ2d 1425, 1433 (Fed. Cir. 2012) ("The computer required by some of Bancorp’s claims is employed only for its most basic function, the performance of repetitive calculations, and as such does not impose meaningful limits on the scope of those claims."); and storing and retrieving information in memory, Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015); OIP Techs., 788 F.3d at 1363, 115 USPQ2d at 1092-93; see MPEP 2106.05(d)(II).
Therefore, the claims at issue do not require any nonconventional computer, network, or display components, or even a “non-conventional and non-generic arrangement of know, conventional pieces,” but merely call for performance of the claimed on a set of generic computer components” and display devices. All of these additional elements are significantly more because these, again, are merely the software and/or hardware components used to implement the abstract idea on a general-purpose computer. Generically recited computer elements do not add a meaningful limitation to the abstract idea because the Alice decision noted that generic structures that merely apply abstract ideas are not significantly more than the abstract ideas.
The computing elements with a computing device is recited at high level of generality (e.g. a generic device performing a generic computer function of processing data). Thus, this step is no more than mere instructions to apply the exception on a generic computer. In addition, using a processor to process data has been well- understood routing, conventional activity in the industry for many years. Generic computer features, such as system or storage, do not amount to significantly more than the abstract idea. These limitations merely describe implementation for the invention using elements of a general-purpose system, which is not sufficient to amount to significantly more. See, e.g., Alice Corp., 134 S. Ct. 2347, 110 USPQ2d 1976; Versata Dev. Group, Inc. v. SAP Am. Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1791 (Federal Circuit 2015).
The claim fails to recite any improvements to another technology or technical field, improvements to the functioning of the computer itself, use of a particular machine, effecting a transformation or reduction of a particular article to a different state or thing, adding unconventional steps that confine the claim to a particular useful application, and/or meaningful limitations beyond generally linking the use of an abstract idea to a particular environment. See 84 Fed. Reg. 55. Viewed individually or as a whole, these additional claim element(s) do not provide meaningful limitation(s) to transform the abstract idea into a patent eligible application of the abstract idea such that the claim(s) amounts to significantly more than the abstract idea itself.
Independent Claims 1 and 8 is/are recite substantially similar limitations to independent claim 7 and is/are rejected under 2B for similar reasons to claim 7 above.
Further, it should be noted that additional elements of the claimed invention such as claim limitations when considered individually or as an ordered combination along with the other limitations discussed above in method claim 7 also do not meaningfully limit the abstract idea because they merely linked the use of the abstract idea to a particular technological environment (i.e., "implementation via computers"). In light of the specification, it should be noted that the claim limitations discussed above are merely instructions to implement the abstract idea on a computer. See MPEP 2106.
Similarly, dependent claims 2-6 and 9-12 also do not include limitations amounting to significantly more than the abstract idea under the second prong or 2B of the Alice framework. In the present application, all of the dependent claims have been evaluated and it was found that they all inherit the deficiencies set forth with respect to the independent claims. Further, it should be noted that the dependent claims do not include limitations that overcome the stated assertions. Here, the dependent claims recite features/limitations that include computer components identified above in part 2B of analysis of independent claims 1, 7-8. As a result, Examiner asserts that dependent claims, such as dependent claims 2-6 and 9-12 are also directed to the abstract idea identified above.
Further, Examiner notes that the addition limitations, when considered as an ordered combination, add nothing that is not already present when looking at the additional elements individually.
For more information on 101 rejections, see MPEP 2106, January 2019 Guidance at https://www.govinfo.gov/content/pkg/FR-2019-01 -07/pdf/2018-28282.pdf
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.
Claim(s) 1-12 is/are rejected under 35 U.S.C. 103 as being unpatentable over (US 2023/0147573 A1) Chien et al., and further in view of (US 11,928,438 B1) Das et al.
As per claims 1, 7 and 8: Regarding the claim limitations below, Reference Chien shows:
An information processing apparatus comprising (Reference Chien: [0003] In one example, the present disclosure describes a method, computer-readable medium, and apparatus for reporting a disposition of a first zone identified based upon sensor data from a plurality of sensor devices applied to at least one detection model. For example, a processing system including at least one processor may collect sensor data for a first zone via a plurality of sensor devices deployed in the first zone in communication with the processing system, where the plurality of sensor devices comprises at least one of a camera or a microphone, and where the sensor data is collected over a period of time. The processing system may next identify that a first disposition is associated with the first zone based upon the sensor data, where the identifying comprises applying at least one detection model to the sensor data, where the at least one detection model is configured to output at least one disposition based upon the sensor data as input data to the at least one detection model, and where the at least one disposition comprises the first disposition. The sensor data collected over the period of time may comprise a plurality of inputs to the at least one detection model, and the identifying that the first disposition is associated with the first zone may include aggregating a plurality of outputs of the at least one detection model from the plurality of inputs. The processing system may then report that the first disposition is associated with the first zone):
Regarding the claim limitations below, Reference Chien shows:
An information processing method comprising (Reference Chien: [0003] In one example, the present disclosure describes a method, computer-readable medium, and apparatus for reporting a disposition of a first zone identified based upon sensor data from a plurality of sensor devices applied to at least one detection model. For example, a processing system including at least one processor may collect sensor data for a first zone via a plurality of sensor devices deployed in the first zone in communication with the processing system, where the plurality of sensor devices comprises at least one of a camera or a microphone, and where the sensor data is collected over a period of time. The processing system may next identify that a first disposition is associated with the first zone based upon the sensor data, where the identifying comprises applying at least one detection model to the sensor data, where the at least one detection model is configured to output at least one disposition based upon the sensor data as input data to the at least one detection model, and where the at least one disposition comprises the first disposition. The sensor data collected over the period of time may comprise a plurality of inputs to the at least one detection model, and the identifying that the first disposition is associated with the first zone may include aggregating a plurality of outputs of the at least one detection model from the plurality of inputs. The processing system may then report that the first disposition is associated with the first zone):
Regarding the claim limitations below, Reference Chien shows:
A non-transitory computer readable storage medium storing an information processing program for causing a computer to execute (Reference Chien: [0003] In one example, the present disclosure describes a method, computer-readable medium, and apparatus for reporting a disposition of a first zone identified based upon sensor data from a plurality of sensor devices applied to at least one detection model. For example, a processing system including at least one processor may collect sensor data for a first zone via a plurality of sensor devices deployed in the first zone in communication with the processing system, where the plurality of sensor devices comprises at least one of a camera or a microphone, and where the sensor data is collected over a period of time. The processing system may next identify that a first disposition is associated with the first zone based upon the sensor data, where the identifying comprises applying at least one detection model to the sensor data, where the at least one detection model is configured to output at least one disposition based upon the sensor data as input data to the at least one detection model, and where the at least one disposition comprises the first disposition. The sensor data collected over the period of time may comprise a plurality of inputs to the at least one detection model, and the identifying that the first disposition is associated with the first zone may include aggregating a plurality of outputs of the at least one detection model from the plurality of inputs. The processing system may then report that the first disposition is associated with the first zone):
Regarding the claim limitations below, Reference Chien shows:
an acquisition unit that acquires user information about a user who has visited at least one of a plurality of places included in a predetermined area (Reference Chien: 0028] In an illustrative example, server(s) 116 may generate a disposition profile of zone 1 in accordance with one or more dispositions of zone 1 determined based upon sensor data from various sensor devices, e.g., including at least one of camera 141 or microphone 143, and in one example further including AQS 146 and/or WQS 147, sensor data from sensors of UAV 160 and/or mobile sensor station 170, and so forth. For instance, camera 141 may collect image data (e.g., video and/or still images) which may appear to include a number of people gathered in a park playing baseball. In one example, server(s) 116 may apply detection models (e.g., MLMs or the like stored in DB(s) 118) for detecting semantic content in video, such as “baseball,” “exercise,” “crowd,” “car,” “traffic,” etc. In one example, the semantic content may be mapped to various dispositions from a defined set of dispositions. For instance, detected “sports” and “recreation” can be mapped to one or more dispositions of “vibrant,” “healthy,” etc. In another example, server(s) 116 may apply detection models for semantic content, where the semantic content comprises the dispositions from the defined set of dispositions. For instance, server(s) 116 may deploy detection models for dispositions of: “sensitive,” “proud,” “extroverted,” “friendly,” “curious,” “dour,” “content,” “restless,” “driven,” “relaxed,” “uptight,” and so forth. For example, in such case, the dispositions may be identified more directly from the captured image data using detection models, without intermediate determination of other types of semantic content and then mapping into associated dispositions. In one example, server(s) 116 may apply detection models for detecting human faces (and emotional states thereof) in image data from camera 141 or the like. In one example, the emotional states may comprise dispositions from the defined set of dispositions. In another example, the emotional states may comprise a larger set of emotional states that may be mapped to dispositions from the defined set of dispositions. [0035] It should be noted that as referred to herein, a machine learning model (MLM) (or machine learning-based model) may comprise a machine learning algorithm (MLA) that has been “trained” or configured in accordance with input training data to perform a particular service, e.g., to detect a perceived disposition, a perceived mental state, mood, or emotional state, or other semantic content, or a value indicative of such a perceived disposition, mental state, mood, etc. In one example, MLM-based detection models associated with image data inputs may be trained using samples of video or still images that may be labeled by participants or by human observers with dispositions (and/or with other semantic content labels/tags). For instance, a machine learning algorithm (MLA), or machine learning model (MLM) trained via a MLA may be for detecting a single semantic concept, such as a disposition, or may be for detecting a single semantic concept from a plurality of possible semantic concepts that may be detected via the MLA/MLM (e.g., a set of dispositions). [0051] In still another example, a button may be included or a requester may otherwise select an input to obtain a zone disposition profile that is scaled in accordance with a personal profile of the requester. For instance, in one example, in addition to learning and storing zone disposition profiles, the present disclosure may also learn, store, and utilize personal profiles of requesters. To illustrate, a requester may have a unique perspective with different opinions from others as to what is considered “vibrant,” what is considered “stressed,” what is considered “abrasive,” what is considered “very vibrant” or “very stressed,” etc. The requester's perspective may be cultural, may be formed based upon a type of region in which the requester lived as a child, a type of region in which the requester has most recently lived, a marital status, a status of a number of children (or having no children), may be formed based upon general personality characteristics of the requester (e.g., introverted vs. extroverted, curious vs. not curious, relaxed vs. stressed, etc.), and so forth.);
Regarding the claim limitations below, Reference Chien in view of Reference Das shows:
an estimation step of using a generative model trained to output a response to an input question to:
(Reference Chien shows “an estimation unit that uses … to output a response to an input question to”: [0028] In an illustrative example, server(s) 116 may generate a disposition profile of zone 1 in accordance with one or more dispositions of zone 1 determined based upon sensor data from various sensor devices, e.g., including at least one of camera 141 or microphone 143, and in one example further including AQS 146 and/or WQS 147, sensor data from sensors of UAV 160 and/or mobile sensor station 170, and so forth. For instance, camera 141 may collect image data (e.g., video and/or still images) which may appear to include a number of people gathered in a park playing baseball. In one example, server(s) 116 may apply detection models (e.g., MLMs or the like stored in DB(s) 118) for detecting semantic content in video, such as “baseball,” “exercise,” “crowd,” “car,” “traffic,” etc. In one example, the semantic content may be mapped to various dispositions from a defined set of dispositions. For instance, detected “sports” and “recreation” can be mapped to one or more dispositions of “vibrant,” “healthy,” etc. In another example, server(s) 116 may apply detection models for semantic content, where the semantic content comprises the dispositions from the defined set of dispositions. For instance, server(s) 116 may deploy detection models for dispositions of: “sensitive,” “proud,” “extroverted,” “friendly,” “curious,” “dour,” “content,” “restless,” “driven,” “relaxed,” “uptight,” and so forth. For example, in such case, the dispositions may be identified more directly from the captured image data using detection models, without intermediate determination of other types of semantic content and then mapping into associated dispositions. In one example, server(s) 116 may apply detection models for detecting human faces (and emotional states thereof) in image data from camera 141 or the like. In one example, the emotional states may comprise dispositions from the defined set of dispositions. In another example, the emotional states may comprise a larger set of emotional states that may be mapped to dispositions from the defined set of dispositions. [0035] It should be noted that as referred to herein, a machine learning model (MLM) (or machine learning-based model) may comprise a machine learning algorithm (MLA) that has been “trained” or configured in accordance with input training data to perform a particular service, e.g., to detect a perceived disposition, a perceived mental state, mood, or emotional state, or other semantic content, or a value indicative of such a perceived disposition, mental state, mood, etc. In one example, MLM-based detection models associated with image data inputs may be trained using samples of video or still images that may be labeled by participants or by human observers with dispositions (and/or with other semantic content labels/tags). For instance, a machine learning algorithm (MLA), or machine learning model (MLM) trained via a MLA may be for detecting a single semantic concept, such as a disposition, or may be for detecting a single semantic concept from a plurality of possible semantic concepts that may be detected via the MLA/MLM (e.g., a set of dispositions). [0051] In still another example, a button may be included or a requester may otherwise select an input to obtain a zone disposition profile that is scaled in accordance with a personal profile of the requester. For instance, in one example, in addition to learning and storing zone disposition profiles, the present disclosure may also learn, store, and utilize personal profiles of requesters. To illustrate, a requester may have a unique perspective with different opinions from others as to what is considered “vibrant,” what is considered “stressed,” what is considered “abrasive,” what is considered “very vibrant” or “very stressed,” etc. The requester's perspective may be cultural, may be formed based upon a type of region in which the requester lived as a child, a type of region in which the requester has most recently lived, a marital status, a status of a number of children (or having no children), may be formed based upon general personality characteristics of the requester (e.g., introverted vs. extroverted, curious vs. not curious, relaxed vs. stressed, etc.), and so forth.
Although Reference Chien discloses gathering information about an area from users and adding context to the data ([0028], [0035], and [0051]), Reference Chien also shows machine learning models and neural networks in [0027], [0035], [0037], [0043], [0054], [0064], however, Reference Chien does not disclose “generative model trained”. Reference Das discloses “generative model trained” (Col. 5, lines 12-27: The LLM 110 may be a language model (e.g., a generative artificial intelligence (AI) model, a generative adversarial network (GAN) model, a generative pre-trained transformer (GPT) model) including an artificial neural network (ANN) with a set of parameters (e.g., tens of weight, hundreds of weights, thousands of weights, millions of weights, billions of weights, trillions of weights), initially trained on a quantity of unlabeled content (e.g., text, unstructured text, descriptive text, imagery, sounds) using a self-supervised learning algorithm or a semi-supervised learning algorithm to understand a set of corresponding data relationships. Then, the LLM 110 may be further trained by fine-tuning or refining the set of corresponding data relationships via a supervised learning algorithm or a reinforcement learning algorithm).
Reference Chien and Reference Das are analogous prior art to the claimed invention because the references generally relate to field of gathering large amounts of data and then using machine learning models analyzing it. Further, said references are part of the same classification, i.e., G06Q. Lastly, said references are filed before the effective filing date of the instant application; hence, said references are analogous prior-art references.
It would have been obvious to one of ordinary skill in the art before the effective filing date of this application for AIA to provide the teachings of Reference Das, particularly the ability to use generative model trained (Col. 5, lines 12-27), in the disclosure of Reference Chien, particularly in the gathering information about an area from users and adding context to the data ([0028], [0035], and [0051]), Reference Chien also shows machine learning models and neural networks in [0027], [0035], [0037], [0043], [0054], [0064], in order to provide for a system that uses generative models initially trained on a quantity of unlabeled content (e.g., text, unstructured text, descriptive text, imagery, sounds) using a self-supervised learning algorithm or a semi-supervised learning algorithm to understand a set of corresponding data relationships as taught by Reference Das (see at least in Col. 5, lines 12-27), where upon the execution of the method and system of Reference Das for improving functioning of computers by enabling large language models to review bytecodes executable on distributed ledgers to determine whether those bytecodes enable smart contracts on those distributed ledgers and then acting accordingly (Reference Das: col. 1, lines 40-47) so that the process of gathering large amounts of data and then using machine learning models analyzing it can be made more efficient and effective.
Further, the claimed invention is merely a combination of old elements in a similar gathering large amounts of data and then using machine learning models analyzing it field of endeavor, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that, given the existing technical ability to combine the elements as evidenced by Reference Chien in view of Reference Das, the results of the combination were predictable (MPEP 2143 A); and
Regarding the claim limitations below, Reference Chien in view of Reference Das shows:
extract, for each place included in the predetermined area, characteristics of the place from area information, movement or behavior associated with the user who has visited the place, and data about an interest of the user
(Reference Chien shows [0028] In an illustrative example, server(s) 116 may generate a disposition profile of zone 1 in accordance with one or more dispositions of zone 1 determined based upon sensor data from various sensor devices, e.g., including at least one of camera 141 or microphone 143, and in one example further including AQS 146 and/or WQS 147, sensor data from sensors of UAV 160 and/or mobile sensor station 170, and so forth. For instance, camera 141 may collect image data (e.g., video and/or still images) which may appear to include a number of people gathered in a park playing baseball. In one example, server(s) 116 may apply detection models (e.g., MLMs or the like stored in DB(s) 118) for detecting semantic content in video, such as “baseball,” “exercise,” “crowd,” “car,” “traffic,” etc. In one example, the semantic content may be mapped to various dispositions from a defined set of dispositions. For instance, detected “sports” and “recreation” can be mapped to one or more dispositions of “vibrant,” “healthy,” etc. In another example, server(s) 116 may apply detection models for semantic content, where the semantic content comprises the dispositions from the defined set of dispositions. For instance, server(s) 116 may deploy detection models for dispositions of: “sensitive,” “proud,” “extroverted,” “friendly,” “curious,” “dour,” “content,” “restless,” “driven,” “relaxed,” “uptight,” and so forth. For example, in such case, the dispositions may be identified more directly from the captured image data using detection models, without intermediate determination of other types of semantic content and then mapping into associated dispositions. In one example, server(s) 116 may apply detection models for detecting human faces (and emotional states thereof) in image data from camera 141 or the like. In one example, the emotional states may comprise dispositions from the defined set of dispositions. In another example, the emotional states may comprise a larger set of emotional states that may be mapped to dispositions from the defined set of dispositions. [0035] It should be noted that as referred to herein, a machine learning model (MLM) (or machine learning-based model) may comprise a machine learning algorithm (MLA) that has been “trained” or configured in accordance with input training data to perform a particular service, e.g., to detect a perceived disposition, a perceived mental state, mood, or emotional state, or other semantic content, or a value indicative of such a perceived disposition, mental state, mood, etc. In one example, MLM-based detection models associated with image data inputs may be trained using samples of video or still images that may be labeled by participants or by human observers with dispositions (and/or with other semantic content labels/tags). For instance, a machine learning algorithm (MLA), or machine learning model (MLM) trained via a MLA may be for detecting a single semantic concept, such as a disposition, or may be for detecting a single semantic concept from a plurality of possible semantic concepts that may be detected via the MLA/MLM (e.g., a set of dispositions). [0051] In still another example, a button may be included or a requester may otherwise select an input to obtain a zone disposition profile that is scaled in accordance with a personal profile of the requester. For instance, in one example, in addition to learning and storing zone disposition profiles, the present disclosure may also learn, store, and utilize personal profiles of requesters. To illustrate, a requester may have a unique perspective with different opinions from others as to what is considered “vibrant,” what is considered “stressed,” what is considered “abrasive,” what is considered “very vibrant” or “very stressed,” etc. The requester's perspective may be cultural, may be formed based upon a type of region in which the requester lived as a child, a type of region in which the requester has most recently lived, a marital status, a status of a number of children (or having no children), may be formed based upon general personality characteristics of the requester (e.g., introverted vs. extroverted, curious vs. not curious, relaxed vs. stressed, etc.), and so forth; and
Regarding the claim limitations below, Reference Chien in view of Reference Das shows:
cluster places having similar characteristics into a cluster, and
(Reference Chien shows: [0028] In an illustrative example, server(s) 116 may generate a disposition profile of zone 1 in accordance with one or more dispositions of zone 1 determined based upon sensor data from various sensor devices, e.g., including at least one of camera 141 or microphone 143, and in one example further including AQS 146 and/or WQS 147, sensor data from sensors of UAV 160 and/or mobile sensor station 170, and so forth. For instance, camera 141 may collect image data (e.g., video and/or still images) which may appear to include a number of people gathered in a park playing baseball. In one example, server(s) 116 may apply detection models (e.g., MLMs or the like stored in DB(s) 118) for detecting semantic content in video, such as “baseball,” “exercise,” “crowd,” “car,” “traffic,” etc. In one example, the semantic content may be mapped to various dispositions from a defined set of dispositions. For instance, detected “sports” and “recreation” can be mapped to one or more dispositions of “vibrant,” “healthy,” etc. In another example, server(s) 116 may apply detection models for semantic content, where the semantic content comprises the dispositions from the defined set of dispositions. For instance, server(s) 116 may deploy detection models for dispositions of: “sensitive,” “proud,” “extroverted,” “friendly,” “curious,” “dour,” “content,” “restless,” “driven,” “relaxed,” “uptight,” and so forth. For example, in such case, the dispositions may be identified more directly from the captured image data using detection models, without intermediate determination of other types of semantic content and then mapping into associated dispositions. In one example, server(s) 116 may apply detection models for detecting human faces (and emotional states thereof) in image data from camera 141 or the like. In one example, the emotional states may comprise dispositions from the defined set of dispositions. In another example, the emotional states may comprise a larger set of emotional states that may be mapped to dispositions from the defined set of dispositions. [0035] It should be noted that as referred to herein, a machine learning model (MLM) (or machine learning-based model) may comprise a machine learning algorithm (MLA) that has been “trained” or configured in accordance with input training data to perform a particular service, e.g., to detect a perceived disposition, a perceived mental state, mood, or emotional state, or other semantic content, or a value indicative of such a perceived disposition, mental state, mood, etc. In one example, MLM-based detection models associated with image data inputs may be trained using samples of video or still images that may be labeled by participants or by human observers with dispositions (and/or with other semantic content labels/tags). For instance, a machine learning algorithm (MLA), or machine learning model (MLM) trained via a MLA may be for detecting a single semantic concept, such as a disposition, or may be for detecting a single semantic concept from a plurality of possible semantic concepts that may be detected via the MLA/MLM (e.g., a set of dispositions). [0051] In still another example, a button may be included or a requester may otherwise select an input to obtain a zone disposition profile that is scaled in accordance with a personal profile of the requester. For instance, in one example, in addition to learning and storing zone disposition profiles, the present disclosure may also learn, store, and utilize personal profiles of requesters. To illustrate, a requester may have a unique perspective with different opinions from others as to what is considered “vibrant,” what is considered “stressed,” what is considered “abrasive,” what is considered “very vibrant” or “very stressed,” etc. The requester's perspective may be cultural, may be formed based upon a type of region in which the requester lived as a child, a type of region in which the requester has most recently lived, a marital status, a status of a number of children (or having no children), may be formed based upon general personality characteristics of the requester (e.g., introverted vs. extroverted, curious vs. not curious, relaxed vs. stressed, etc.), and so forth. [0035]: It should be noted that various other types of MLAs and/or MLMs, or other detection models may be implemented in examples of the present disclosure such as a gradient boosted decision tree (GBDT), k-means clustering and/or k-nearest neighbor (KNN) predictive models, support vector machine (SVM)-based classifiers, e.g., a binary classifier and/or a linear binary classifier, a multi-class classifier, a kernel-based SVM, etc., a distance-based classifier, e.g., a Euclidean distance-based classifier, or the like, a SIFT or SURF features-based detection model, as mentioned above, and so on. In one example, MLM-based detection models may be trained at a network-based processing system (e.g., server(s) 116) and deployed to sensor devices, such as cameras 141 and 151, microphones 143 and 153, etc.). Similarly, non-MLM-based detection models may be generated by server(s) 116, e.g., based upon feature sets from sample input data as described above. It should also be noted that various pre-processing or post-recognition/detection operations may also be applied. For example, server(s) 116 may apply an image salience algorithm, an edge detection algorithm, or the like (e.g., as described above) where the results of these algorithms may include additional, or pre-processed input data for the one or more detection models. [0065] In one example, the method 300 can include defining zones based upon landmarks, or accepting one or more inputs for user-defined neighborhoods or zones. For instance, city planners may use this results from step 350 for various purposes and may define a neighborhood as a zone for investigative purposes. In another instance, the results can be used to ascertain the mood of a large gathering of people to detect potential security or safety risks, e.g., the mood of a large crowd celebrating a sporting event outcome. However, in another example, this can automatically be done by the processing system to account for population density or perceived population density, a perceived basis for geographic grouping (or multiple factors indicative of geographic grouping), a number of available sensors in an area, etc. In one example, the method 300 may include obtaining and providing at step 350 additional data along with disposition information, such as a type of area (residential, commercial, office, industrial, recreational, etc.), an estimated density of people in the zone, e.g., based upon existing available census and demographic data or estimated in other ways, such as average number of unique detected mobile endpoint devices in the zone, and so forth. In one example, density of people may be broken down by morning, afternoon, evening, or hours of the day, days of the week, days, months, seasons or other times of the year, and so forth. In such an example, the method 300 may include obtaining a user/requester selection of a time period of interest for the reporting);
Regarding the claim limitations below, Reference Chien in view of Reference Das shows:
output, in accordance with an output format, the target area, a title of the cluster, and a description of the cluster as the characteristic of the extracted target area, wherein the output format specifies the target area by a latitude and longitude, and wherein the cluster comprises the target area; and
(Reference Chien shows: [0028] In an illustrative example, server(s) 116 may generate a disposition profile of zone 1 in accordance with one or more dispositions of zone 1 determined based upon sensor data from various sensor devices, e.g., including at least one of camera 141 or microphone 143, and in one example further including AQS 146 and/or WQS 147, sensor data from sensors of UAV 160 and/or mobile sensor station 170, and so forth. For instance, camera 141 may collect image data (e.g., video and/or still images) which may appear to include a number of people gathered in a park playing baseball. In one example, server(s) 116 may apply detection models (e.g., MLMs or the like stored in DB(s) 118) for detecting semantic content in video, such as “baseball,” “exercise,” “crowd,” “car,” “traffic,” etc. In one example, the semantic content may be mapped to various dispositions from a defined set of dispositions. For instance, detected “sports” and “recreation” can be mapped to one or more dispositions of “vibrant,” “healthy,” etc. In another example, server(s) 116 may apply detection models for semantic content, where the semantic content comprises the dispositions from the defined set of dispositions. For instance, server(s) 116 may deploy detection models for dispositions of: “sensitive,” “proud,” “extroverted,” “friendly,” “curious,” “dour,” “content,” “restless,” “driven,” “relaxed,” “uptight,” and so forth. For example, in such case, the dispositions may be identified more directly from the captured image data using detection models, without intermediate determination of other types of semantic content and then mapping into associated dispositions. In one example, server(s) 116 may apply detection models for detecting human faces (and emotional states thereof) in image data from camera 141 or the like. In one example, the emotional states may comprise dispositions from the defined set of dispositions. In another example, the emotional states may comprise a larger set of emotional states that may be mapped to dispositions from the defined set of dispositions. [0035] It should be noted that as referred to herein, a machine learning model (MLM) (or machine learning-based model) may comprise a machine learning algorithm (MLA) that has been “trained” or configured in accordance with input training data to perform a particular service, e.g., to detect a perceived disposition, a perceived mental state, mood, or emotional state, or other semantic content, or a value indicative of such a perceived disposition, mental state, mood, etc. In one example, MLM-based detection models associated with image data inputs may be trained using samples of video or still images that may be labeled by participants or by human observers with dispositions (and/or with other semantic content labels/tags). For instance, a machine learning algorithm (MLA), or machine learning model (MLM) trained via a MLA may be for detecting a single semantic concept, such as a disposition, or may be for detecting a single semantic concept from a plurality of possible semantic concepts that may be detected via the MLA/MLM (e.g., a set of dispositions). [0051] In still another example, a button may be included or a requester may otherwise select an input to obtain a zone disposition profile that is scaled in accordance with a personal profile of the requester. For instance, in one example, in addition to learning and storing zone disposition profiles, the present disclosure may also learn, store, and utilize personal profiles of requesters. To illustrate, a requester may have a unique perspective with different opinions from others as to what is considered “vibrant,” what is considered “stressed,” what is considered “abrasive,” what is considered “very vibrant” or “very stressed,” etc. The requester's perspective may be cultural, may be formed based upon a type of region in which the requester lived as a child, a type of region in which the requester has most recently lived, a marital status, a status of a number of children (or having no children), may be formed based upon general personality characteristics of the requester (e.g., introverted vs. extroverted, curious vs. not curious, relaxed vs. stressed, etc.), and so forth. [0022] In one example, device 114 may comprise a mobile device, a cellular smart phone, a laptop, a tablet computer, a desktop computer, a wearable computing device (e.g., a smart watch, a smart pair of eyeglasses, etc.), an application server, a bank or cluster of such devices, or the like. Similarly, mobile device 115 may comprise a cellular smart phone, a laptop, a tablet computer, a wearable computing device (e.g., a smart watch, a smart pair of eyeglasses, etc.), or the like. In accordance with the present disclosure, mobile device 115 may include one or more sensors for tracking location, speed, distance, altitude, or the like (e.g., a Global Positioning System (GPS) unit), for tracking orientation (e.g., gyroscope and compass), and so forth. Cameras 141 and 151 may comprise publicly deployed cameras such as traffic cameras, security cameras, and so forth. Microphones 143 and 153, air quality sensors 146 and 156, and water quality sensors 147 and 157 may similarly be network-connected “Internet of Things” (IoT) devices. Although omitted from FIG. 1, for ease of illustrate, sensor devices may also include humidity sensors, thermometers, rain sensors, motion detectors, vibration detectors, and so forth. [0027] In one example, DB(s) 118 may comprise one or more physical storage devices integrated with server(s) 116 (e.g., a database server), attached or coupled to the server(s) 116, or remotely accessible to server(s) 116 to store various types of information in support of systems for reporting a disposition of a first zone identified based upon sensor data from a plurality of sensor devices applied to at least one detection model, in accordance with the present disclosure. For example, DB(s) 118 may include a sensor database to store a record for each sensor that may include: a sensor identifier (ID), a network address of the sensor, sensor owner information, a sensor type and/or the type(s) of data the sensor is capable of collecting, a fixed location (for a non-mobile sensor), the sensor availability (e.g., dates, data or time ranges, etc.), and for a mobile sensor, the sensor's range, operating time (e.g., without recharging or refueling, etc.), a current location, and so on. DB(s) 118 may also temporally store collected sensor data (e.g., in the sensor database or in a separate database). In addition, DB(s) 118 may comprise one or more geographic databases, e.g., storing maps and/or geographic data sets. For instance, DB(s) 118 may store a map/geographic data set for area 190, which may include information regarding zones 1 and 2, such as boundary point/coordinate sets or similar descriptors. In one example, DB(s) 118 may also store a database of detection models, e.g., machine learning models (MLMs) or the like, for detecting semantic content in video and/or audio data, for associating sensor data to dispositions, and so forth);
Regarding the claim limitations below, Reference Chien in view of Reference Das shows:
a display step of displaying the characteristic of the target area estimated in the estimation step as summary content including map data and a message, by performing mapping on the map data and distinguishing the mapping by shape or color to visualize a range of the target area on a map.
(Reference Chien shows: [0028] In an illustrative example, server(s) 116 may generate a disposition profile of zone 1 in accordance with one or more dispositions of zone 1 determined based upon sensor data from various sensor devices, e.g., including at least one of camera 141 or microphone 143, and in one example further including AQS 146 and/or WQS 147, sensor data from sensors of UAV 160 and/or mobile sensor station 170, and so forth. For instance, camera 141 may collect image data (e.g., video and/or still images) which may appear to include a number of people gathered in a park playing baseball. In one example, server(s) 116 may apply detection models (e.g., MLMs or the like stored in DB(s) 118) for detecting semantic content in video, such as “baseball,” “exercise,” “crowd,” “car,” “traffic,” etc. In one example, the semantic content may be mapped to various dispositions from a defined set of dispositions. For instance, detected “sports” and “recreation” can be mapped to one or more dispositions of “vibrant,” “healthy,” etc. In another example, server(s) 116 may apply detection models for semantic content, where the semantic content comprises the dispositions from the defined set of dispositions. For instance, server(s) 116 may deploy detection models for dispositions of: “sensitive,” “proud,” “extroverted,” “friendly,” “curious,” “dour,” “content,” “restless,” “driven,” “relaxed,” “uptight,” and so forth. For example, in such case, the dispositions may be identified more directly from the captured image data using detection models, without intermediate determination of other types of semantic content and then mapping into associated dispositions. In one example, server(s) 116 may apply detection models for detecting human faces (and emotional states thereof) in image data from camera 141 or the like. In one example, the emotional states may comprise dispositions from the defined set of dispositions. In another example, the emotional states may comprise a larger set of emotional states that may be mapped to dispositions from the defined set of dispositions. [0035] It should be noted that as referred to herein, a machine learning model (MLM) (or machine learning-based model) may comprise a machine learning algorithm (MLA) that has been “trained” or configured in accordance with input training data to perform a particular service, e.g., to detect a perceived disposition, a perceived mental state, mood, or emotional state, or other semantic content, or a value indicative of such a perceived disposition, mental state, mood, etc. In one example, MLM-based detection models associated with image data inputs may be trained using samples of video or still images that may be labeled by participants or by human observers with dispositions (and/or with other semantic content labels/tags). For instance, a machine learning algorithm (MLA), or machine learning model (MLM) trained via a MLA may be for detecting a single semantic concept, such as a disposition, or may be for detecting a single semantic concept from a plurality of possible semantic concepts that may be detected via the MLA/MLM (e.g., a set of dispositions). [0051] In still another example, a button may be included or a requester may otherwise select an input to obtain a zone disposition profile that is scaled in accordance with a personal profile of the requester. For instance, in one example, in addition to learning and storing zone disposition profiles, the present disclosure may also learn, store, and utilize personal profiles of requesters. To illustrate, a requester may have a unique perspective with different opinions from others as to what is considered “vibrant,” what is considered “stressed,” what is considered “abrasive,” what is considered “very vibrant” or “very stressed,” etc. The requester's perspective may be cultural, may be formed based upon a type of region in which the requester lived as a child, a type of region in which the requester has most recently lived, a marital status, a status of a number of children (or having no children), may be formed based upon general personality characteristics of the requester (e.g., introverted vs. extroverted, curious vs. not curious, relaxed vs. stressed, etc.), and so forth. Reference Chien: [0059] For instance, the aggregating may comprise tallying the plurality of outputs associated with each of a plurality of dispositions from a defined set of dispositions. For example, the first disposition may comprise a disposition from the defined set of dispositions having a highest tally count (or from among the top three dispositions, the top four dispositions, etc.), a disposition having a score, tally, or the like above a threshold, and so forth. In other words, the first disposition may be identified as being associated with the zone (e.g., characteristic or representative of the zone) when the first disposition is has a higher tally count that for other dispositions. In one example, the first disposition may be identified as being associated with the first zone when a threshold number or percentage of the plurality of outputs comprises the first disposition. For example, the threshold number or percentage may be based upon a total number of the plurality of inputs. For instance, the processing system may establish that a minimum number of samples of sensor data/input data is required before any dispositions may be considered representative of a zone. In one example, the minimum number of samples may be fixed, or may be based upon a number of people estimated to be present in the zone. For instance, if the first zone is estimated to have 10,000 people, a minimum of 5,000 samples, 10,000 samples, etc. may be required (e.g., 0.5 samples per one person, 1 sample per 1 person, etc. [0033] The visual features used for detection of “baseball game” or other semantic content (such as different types of dispositions, objects/items, events, weather, actions, occurrences, etc.) may include low-level invariant image data, such as colors (e.g., RGB (red-green-blue) or CYM (cyan-yellow-magenta) raw data (luminance values) from a CCD/photo-sensor array), shapes, color moments, color histograms, edge distribution histograms, etc. Visual features may also relate to movement in a video and may include changes within images and between images in a sequence (e.g., video frames or a sequence of still image shots), such as color histogram differences or a change in color distribution, edge change ratios, standard deviation of pixel intensities, contrast, average brightness, and the like. [0034] In one example, server(s) 116 may perform an image salience detection process, e.g., applying an image salience model and then performing an image recognition algorithm over the “salient” portion of the image(s) or other image data/visual information, such as from camera 141 or the like. Thus, in one example, visual features may also include a length to width ratio of an object, a velocity of an object estimated from a sequence of images (e.g., video frames), and so forth. Similarly, in one example, server(s) 116 may apply an object/item detection and/or edge detection algorithm to identify possible unique items in image data (e.g., without particular knowledge of the type of item; for instance, the object/edge detection may identify an object in the shape of a person in a video frame, without understanding that the object/item is a person). In this case, visual features may also include the object/item shape, dimensions, and so forth. In such an example, object/item recognition may then proceed as described above (e.g., with respect to the “salient” portions of the image(s) and/or video(s)). [0050] It should be noted that FIG. 2 illustrates just several examples of how area and zone disposition information may be presented in accordance with a geographic disposition information service of the present disclosure and that other, further, and different example screens and/or user interface(s) may be utilized in various designs. For instance, as illustrated in the example screen 230, a button may be included for “select time period,” which may allow a requester to obtain a disposition profile with regard to times of the day, days of the week, months, seasons of the year, and so forth. For example, a processing system of the present disclosure may generate different disposition profiles for a zone, such as zone 2, for such different time periods based upon historical sensor data collected during such respective time periods. As further illustrated in the example screen 230, a button may be included for “show full disposition profile,” which may allow a requester to view information regarding additional dispositions from a set of possible dispositions (e.g., dispositions beyond the top six that are shown in the example screen 230). In another example, a requester may select a type of disposition, and may be presented with a heat map of zones in an area that may exhibit the disposition. For instance, a requester may wish to see all zones in an area that are considered “stressed” and may be presented with a map that shades, highlights, colors, or otherwise visually indicates the zones that are “stressed” (e.g., zones with values on a stress scale above a threshold, zones with stress being one of the top three dispositions for the particular zone, etc.).)
As per claim 2: Regarding the claim limitations below, Reference Chien in view of Reference Das shows:
wherein the estimation unit extracts, from among places included in the predetermined area, a common place in the user information, as the target area, and estimates a characteristic of the extracted target area based on the user information about the user who has visited the target area.
Reference Chien shows “an estimation unit that uses … to output a response to an input question to extract a characteristic place included in the predetermined area as a target area, from user information acquired by the acquisition unit, and estimates a characteristic of the extracted target area”: [0028] In an illustrative example, server(s) 116 may generate a disposition profile of zone 1 in accordance with one or more dispositions of zone 1 determined based upon sensor data from various sensor devices, e.g., including at least one of camera 141 or microphone 143, and in one example further including AQS 146 and/or WQS 147, sensor data from sensors of UAV 160 and/or mobile sensor station 170, and so forth. For instance, camera 141 may collect image data (e.g., video and/or still images) which may appear to include a number of people gathered in a park playing baseball. In one example, server(s) 116 may apply detection models (e.g., MLMs or the like stored in DB(s) 118) for detecting semantic content in video, such as “baseball,” “exercise,” “crowd,” “car,” “traffic,” etc. In one example, the semantic content may be mapped to various dispositions from a defined set of dispositions. For instance, detected “sports” and “recreation” can be mapped to one or more dispositions of “vibrant,” “healthy,” etc. In another example, server(s) 116 may apply detection models for semantic content, where the semantic content comprises the dispositions from the defined set of dispositions. For instance, server(s) 116 may deploy detection models for dispositions of: “sensitive,” “proud,” “extroverted,” “friendly,” “curious,” “dour,” “content,” “restless,” “driven,” “relaxed,” “uptight,” and so forth. For example, in such case, the dispositions may be identified more directly from the captured image data using detection models, without intermediate determination of other types of semantic content and then mapping into associated dispositions. In one example, server(s) 116 may apply detection models for detecting human faces (and emotional states thereof) in image data from camera 141 or the like. In one example, the emotional states may comprise dispositions from the defined set of dispositions. In another example, the emotional states may comprise a larger set of emotional states that may be mapped to dispositions from the defined set of dispositions. [0035] It should be noted that as referred to herein, a machine learning model (MLM) (or machine learning-based model) may comprise a machine learning algorithm (MLA) that has been “trained” or configured in accordance with input training data to perform a particular service, e.g., to detect a perceived disposition, a perceived mental state, mood, or emotional state, or other semantic content, or a value indicative of such a perceived disposition, mental state, mood, etc. In one example, MLM-based detection models associated with image data inputs may be trained using samples of video or still images that may be labeled by participants or by human observers with dispositions (and/or with other semantic content labels/tags). For instance, a machine learning algorithm (MLA), or machine learning model (MLM) trained via a MLA may be for detecting a single semantic concept, such as a disposition, or may be for detecting a single semantic concept from a plurality of possible semantic concepts that may be detected via the MLA/MLM (e.g., a set of dispositions). [0051] In still another example, a button may be included or a requester may otherwise select an input to obtain a zone disposition profile that is scaled in accordance with a personal profile of the requester. For instance, in one example, in addition to learning and storing zone disposition profiles, the present disclosure may also learn, store, and utilize personal profiles of requesters. To illustrate, a requester may have a unique perspective with different opinions from others as to what is considered “vibrant,” what is considered “stressed,” what is considered “abrasive,” what is considered “very vibrant” or “very stressed,” etc. The requester's perspective may be cultural, may be formed based upon a type of region in which the requester lived as a child, a type of region in which the requester has most recently lived, a marital status, a status of a number of children (or having no children), may be formed based upon general personality characteristics of the requester (e.g., introverted vs. extroverted, curious vs. not curious, relaxed vs. stressed, etc.), and so forth.
Although Reference Chien discloses gathering information about an area from users and adding context to the data ([0028], [0035], and [0051]), Reference Chien also shows machine learning models and neural networks in [0027], [0035], [0037], [0043], [0054], [0064], however, Reference Chien does not disclose “generative model trained”. Reference Das discloses “generative model trained” (Col. 5, lines 12-27: The LLM 110 may be a language model (e.g., a generative artificial intelligence (AI) model, a generative adversarial network (GAN) model, a generative pre-trained transformer (GPT) model) including an artificial neural network (ANN) with a set of parameters (e.g., tens of weight, hundreds of weights, thousands of weights, millions of weights, billions of weights, trillions of weights), initially trained on a quantity of unlabeled content (e.g., text, unstructured text, descriptive text, imagery, sounds) using a self-supervised learning algorithm or a semi-supervised learning algorithm to understand a set of corresponding data relationships. Then, the LLM 110 may be further trained by fine-tuning or refining the set of corresponding data relationships via a supervised learning algorithm or a reinforcement learning algorithm).
Reference Chien and Reference Das are analogous prior art to the claimed invention because the references generally relate to field of gathering large amounts of data and then using machine learning models analyzing it. Further, said references are part of the same classification, i.e., G06Q. Lastly, said references are filed before the effective filing date of the instant application; hence, said references are analogous prior-art references.
It would have been obvious to one of ordinary skill in the art before the effective filing date of this application for AIA to provide the teachings of Reference Das, particularly the ability to use generative model trained (Col. 5, lines 12-27), in the disclosure of Reference Chien, particularly in the gathering information about an area from users and adding context to the data ([0028], [0035], and [0051]), Reference Chien also shows machine learning models and neural networks in [0027], [0035], [0037], [0043], [0054], [0064], in order to provide for a system that uses generative models initially trained on a quantity of unlabeled content (e.g., text, unstructured text, descriptive text, imagery, sounds) using a self-supervised learning algorithm or a semi-supervised learning algorithm to understand a set of corresponding data relationships as taught by Reference Das (see at least in Col. 5, lines 12-27), where upon the execution of the method and system of Reference Das for improving functioning of computers by enabling large language models to review bytecodes executable on distributed ledgers to determine whether those bytecodes enable smart contracts on those distributed ledgers and then acting accordingly (Reference Das: col. 1, lines 40-47) so that the process of gathering large amounts of data and then using machine learning models analyzing it can be made more efficient and effective.
Further, the claimed invention is merely a combination of old elements in a similar gathering large amounts of data and then using machine learning models analyzing it field of endeavor, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that, given the existing technical ability to combine the elements as evidenced by Reference Chien in view of Reference Das, the results of the combination were predictable (MPEP 2143 A).
As per claim 3: Regarding the claim limitations below, Reference Chien in view of Reference Das shows:
wherein the acquisition unit acquires, as the user information, user information about a user who has moved from a predetermined place of departure to a predetermined destination via a predetermined route, and the estimation unit extracts a characteristic place on the predetermined route as a target area, from the user information, and estimates a characteristic of the extracted target area.
(Reference Chien: 0028] In an illustrative example, server(s) 116 may generate a disposition profile of zone 1 in accordance with one or more dispositions of zone 1 determined based upon sensor data from various sensor devices, e.g., including at least one of camera 141 or microphone 143, and in one example further including AQS 146 and/or WQS 147, sensor data from sensors of UAV 160 and/or mobile sensor station 170, and so forth. For instance, camera 141 may collect image data (e.g., video and/or still images) which may appear to include a number of people gathered in a park playing baseball. In one example, server(s) 116 may apply detection models (e.g., MLMs or the like stored in DB(s) 118) for detecting semantic content in video, such as “baseball,” “exercise,” “crowd,” “car,” “traffic,” etc. In one example, the semantic content may be mapped to various dispositions from a defined set of dispositions. For instance, detected “sports” and “recreation” can be mapped to one or more dispositions of “vibrant,” “healthy,” etc. In another example, server(s) 116 may apply detection models for semantic content, where the semantic content comprises the dispositions from the defined set of dispositions. For instance, server(s) 116 may deploy detection models for dispositions of: “sensitive,” “proud,” “extroverted,” “friendly,” “curious,” “dour,” “content,” “restless,” “driven,” “relaxed,” “uptight,” and so forth. For example, in such case, the dispositions may be identified more directly from the captured image data using detection models, without intermediate determination of other types of semantic content and then mapping into associated dispositions. In one example, server(s) 116 may apply detection models for detecting human faces (and emotional states thereof) in image data from camera 141 or the like. In one example, the emotional states may comprise dispositions from the defined set of dispositions. In another example, the emotional states may comprise a larger set of emotional states that may be mapped to dispositions from the defined set of dispositions. [0035] It should be noted that as referred to herein, a machine learning model (MLM) (or machine learning-based model) may comprise a machine learning algorithm (MLA) that has been “trained” or configured in accordance with input training data to perform a particular service, e.g., to detect a perceived disposition, a perceived mental state, mood, or emotional state, or other semantic content, or a value indicative of such a perceived disposition, mental state, mood, etc. In one example, MLM-based detection models associated with image data inputs may be trained using samples of video or still images that may be labeled by participants or by human observers with dispositions (and/or with other semantic content labels/tags). For instance, a machine learning algorithm (MLA), or machine learning model (MLM) trained via a MLA may be for detecting a single semantic concept, such as a disposition, or may be for detecting a single semantic concept from a plurality of possible semantic concepts that may be detected via the MLA/MLM (e.g., a set of dispositions). [0051] In still another example, a button may be included or a requester may otherwise select an input to obtain a zone disposition profile that is scaled in accordance with a personal profile of the requester. For instance, in one example, in addition to learning and storing zone disposition profiles, the present disclosure may also learn, store, and utilize personal profiles of requesters. To illustrate, a requester may have a unique perspective with different opinions from others as to what is considered “vibrant,” what is considered “stressed,” what is considered “abrasive,” what is considered “very vibrant” or “very stressed,” etc. The requester's perspective may be cultural, may be formed based upon a type of region in which the requester lived as a child, a type of region in which the requester has most recently lived, a marital status, a status of a number of children (or having no children), may be formed based upon general personality characteristics of the requester (e.g., introverted vs. extroverted, curious vs. not curious, relaxed vs. stressed, etc.), and so forth.).
As per claim 4: Regarding the claim limitations below, Reference Chien in view of Reference Das shows:
wherein the acquisition unit further acquires characteristic information indicating a characteristic of each place included in the predetermined area, and the estimation unit further extracts the target area based on the characteristic information.
(Reference Chien: 0028] In an illustrative example, server(s) 116 may generate a disposition profile of zone 1 in accordance with one or more dispositions of zone 1 determined based upon sensor data from various sensor devices, e.g., including at least one of camera 141 or microphone 143, and in one example further including AQS 146 and/or WQS 147, sensor data from sensors of UAV 160 and/or mobile sensor station 170, and so forth. For instance, camera 141 may collect image data (e.g., video and/or still images) which may appear to include a number of people gathered in a park playing baseball. In one example, server(s) 116 may apply detection models (e.g., MLMs or the like stored in DB(s) 118) for detecting semantic content in video, such as “baseball,” “exercise,” “crowd,” “car,” “traffic,” etc. In one example, the semantic content may be mapped to various dispositions from a defined set of dispositions. For instance, detected “sports” and “recreation” can be mapped to one or more dispositions of “vibrant,” “healthy,” etc. In another example, server(s) 116 may apply detection models for semantic content, where the semantic content comprises the dispositions from the defined set of dispositions. For instance, server(s) 116 may deploy detection models for dispositions of: “sensitive,” “proud,” “extroverted,” “friendly,” “curious,” “dour,” “content,” “restless,” “driven,” “relaxed,” “uptight,” and so forth. For example, in such case, the dispositions may be identified more directly from the captured image data using detection models, without intermediate determination of other types of semantic content and then mapping into associated dispositions. In one example, server(s) 116 may apply detection models for detecting human faces (and emotional states thereof) in image data from camera 141 or the like. In one example, the emotional states may comprise dispositions from the defined set of dispositions. In another example, the emotional states may comprise a larger set of emotional states that may be mapped to dispositions from the defined set of dispositions. [0035] It should be noted that as referred to herein, a machine learning model (MLM) (or machine learning-based model) may comprise a machine learning algorithm (MLA) that has been “trained” or configured in accordance with input training data to perform a particular service, e.g., to detect a perceived disposition, a perceived mental state, mood, or emotional state, or other semantic content, or a value indicative of such a perceived disposition, mental state, mood, etc. In one example, MLM-based detection models associated with image data inputs may be trained using samples of video or still images that may be labeled by participants or by human observers with dispositions (and/or with other semantic content labels/tags). For instance, a machine learning algorithm (MLA), or machine learning model (MLM) trained via a MLA may be for detecting a single semantic concept, such as a disposition, or may be for detecting a single semantic concept from a plurality of possible semantic concepts that may be detected via the MLA/MLM (e.g., a set of dispositions). [0051] In still another example, a button may be included or a requester may otherwise select an input to obtain a zone disposition profile that is scaled in accordance with a personal profile of the requester. For instance, in one example, in addition to learning and storing zone disposition profiles, the present disclosure may also learn, store, and utilize personal profiles of requesters. To illustrate, a requester may have a unique perspective with different opinions from others as to what is considered “vibrant,” what is considered “stressed,” what is considered “abrasive,” what is considered “very vibrant” or “very stressed,” etc. The requester's perspective may be cultural, may be formed based upon a type of region in which the requester lived as a child, a type of region in which the requester has most recently lived, a marital status, a status of a number of children (or having no children), may be formed based upon general personality characteristics of the requester (e.g., introverted vs. extroverted, curious vs. not curious, relaxed vs. stressed, etc.), and so forth.)
As per claim 5: Regarding the claim limitations below, Reference Chien in view of Reference Das shows:
wherein the estimation unit estimates a content of description about the user who has visited the area, as the characteristic of the target area.
(Reference Chien: 0028] In an illustrative example, server(s) 116 may generate a disposition profile of zone 1 in accordance with one or more dispositions of zone 1 determined based upon sensor data from various sensor devices, e.g., including at least one of camera 141 or microphone 143, and in one example further including AQS 146 and/or WQS 147, sensor data from sensors of UAV 160 and/or mobile sensor station 170, and so forth. For instance, camera 141 may collect image data (e.g., video and/or still images) which may appear to include a number of people gathered in a park playing baseball. In one example, server(s) 116 may apply detection models (e.g., MLMs or the like stored in DB(s) 118) for detecting semantic content in video, such as “baseball,” “exercise,” “crowd,” “car,” “traffic,” etc. In one example, the semantic content may be mapped to various dispositions from a defined set of dispositions. For instance, detected “sports” and “recreation” can be mapped to one or more dispositions of “vibrant,” “healthy,” etc. In another example, server(s) 116 may apply detection models for semantic content, where the semantic content comprises the dispositions from the defined set of dispositions. For instance, server(s) 116 may deploy detection models for dispositions of: “sensitive,” “proud,” “extroverted,” “friendly,” “curious,” “dour,” “content,” “restless,” “driven,” “relaxed,” “uptight,” and so forth. For example, in such case, the dispositions may be identified more directly from the captured image data using detection models, without intermediate determination of other types of semantic content and then mapping into associated dispositions. In one example, server(s) 116 may apply detection models for detecting human faces (and emotional states thereof) in image data from camera 141 or the like. In one example, the emotional states may comprise dispositions from the defined set of dispositions. In another example, the emotional states may comprise a larger set of emotional states that may be mapped to dispositions from the defined set of dispositions. [0035] It should be noted that as referred to herein, a machine learning model (MLM) (or machine learning-based model) may comprise a machine learning algorithm (MLA) that has been “trained” or configured in accordance with input training data to perform a particular service, e.g., to detect a perceived disposition, a perceived mental state, mood, or emotional state, or other semantic content, or a value indicative of such a perceived disposition, mental state, mood, etc. In one example, MLM-based detection models associated with image data inputs may be trained using samples of video or still images that may be labeled by participants or by human observers with dispositions (and/or with other semantic content labels/tags). For instance, a machine learning algorithm (MLA), or machine learning model (MLM) trained via a MLA may be for detecting a single semantic concept, such as a disposition, or may be for detecting a single semantic concept from a plurality of possible semantic concepts that may be detected via the MLA/MLM (e.g., a set of dispositions). [0051] In still another example, a button may be included or a requester may otherwise select an input to obtain a zone disposition profile that is scaled in accordance with a personal profile of the requester. For instance, in one example, in addition to learning and storing zone disposition profiles, the present disclosure may also learn, store, and utilize personal profiles of requesters. To illustrate, a requester may have a unique perspective with different opinions from others as to what is considered “vibrant,” what is considered “stressed,” what is considered “abrasive,” what is considered “very vibrant” or “very stressed,” etc. The requester's perspective may be cultural, may be formed based upon a type of region in which the requester lived as a child, a type of region in which the requester has most recently lived, a marital status, a status of a number of children (or having no children), may be formed based upon general personality characteristics of the requester (e.g., introverted vs. extroverted, curious vs. not curious, relaxed vs. stressed, etc.), and so forth.).
As per claim 6: Regarding the claim limitations below, Reference Chien in view of Reference Das shows:
wherein the display unit displays content in which a result of the estimation by the estimation unit is associated with the target area.
(Reference Chien: [0059] For instance, the aggregating may comprise tallying the plurality of outputs associated with each of a plurality of dispositions from a defined set of dispositions. For example, the first disposition may comprise a disposition from the defined set of dispositions having a highest tally count (or from among the top three dispositions, the top four dispositions, etc.), a disposition having a score, tally, or the like above a threshold, and so forth. In other words, the first disposition may be identified as being associated with the zone (e.g., characteristic or representative of the zone) when the first disposition is has a higher tally count that for other dispositions. In one example, the first disposition may be identified as being associated with the first zone when a threshold number or percentage of the plurality of outputs comprises the first disposition. For example, the threshold number or percentage may be based upon a total number of the plurality of inputs. For instance, the processing system may establish that a minimum number of samples of sensor data/input data is required before any dispositions may be considered representative of a zone. In one example, the minimum number of samples may be fixed, or may be based upon a number of people estimated to be present in the zone. For instance, if the first zone is estimated to have 10,000 people, a minimum of 5,000 samples, 10,000 samples, etc. may be required (e.g., 0.5 samples per one person, 1 sample per 1 person, etc.).
As per claim 9: Regarding the claim limitations below, Reference Chien in view of Reference Das shows:
wherein the estimation unit inputs, to the generative model, a prompt including the user information and an instruction sentence of natural language indicating an instruction for estimation of a characteristic place included in the predetermined area as the target area
(Reference Chien: [0016] Audio data may also be collected anonymously and analyzed to determine dialogue used or terms used that may be indicative of a disposition of the zone. For instance, audio data may be used to determine that various people in a zone are tourists (e.g., detection of a spoken foreign language, detection of a discussion of a known tourist site, etc.), or are young people based on dialogue used or the frequency of their voices. Dialogue analysis may also be used to estimate the temperament or level of happiness of people in an area. For instance, if statements that may be detected as complaints prevail, the level of happiness may be low. Similarly, video analysis may be used to estimate a level of pace in an area. For instance, if people are walking or running at a fast pace, the video analysis may attempt to distinguish between people who are walking at a fast pace to work, which may be a representation of a busy, fast-paced environment (e.g., people with business attire, people carrying briefcases or backpacks, people moving large packages, people pushing a hand truck, etc.), in contrast to people who are detected to be running at a fast pace (e.g., joggers in T-shirts and shorts, joggers with running shoes, etc.), which may be indicative of an active, vibrant, exercise-conscious community. It should be noted that in one example the present disclosure utilizes the sensor data for the sole purpose of identifying dispositions of zones and does not store audio or video data for any longer than necessary for such purpose. In addition, the image or audio data is not used to personally identify any specific individuals or to create a record of any words or actions.).
As per claim 10: Regarding the claim limitations below, Reference Chien in view of Reference Das shows:
wherein the display unit displays the characteristic of the target area as summary content including map data and a message and performs mapping on the map data and distinguishes the mapping by shape or color.
(Reference Chien: [0059] For instance, the aggregating may comprise tallying the plurality of outputs associated with each of a plurality of dispositions from a defined set of dispositions. For example, the first disposition may comprise a disposition from the defined set of dispositions having a highest tally count (or from among the top three dispositions, the top four dispositions, etc.), a disposition having a score, tally, or the like above a threshold, and so forth. In other words, the first disposition may be identified as being associated with the zone (e.g., characteristic or representative of the zone) when the first disposition is has a higher tally count that for other dispositions. In one example, the first disposition may be identified as being associated with the first zone when a threshold number or percentage of the plurality of outputs comprises the first disposition. For example, the threshold number or percentage may be based upon a total number of the plurality of inputs. For instance, the processing system may establish that a minimum number of samples of sensor data/input data is required before any dispositions may be considered representative of a zone. In one example, the minimum number of samples may be fixed, or may be based upon a number of people estimated to be present in the zone. For instance, if the first zone is estimated to have 10,000 people, a minimum of 5,000 samples, 10,000 samples, etc. may be required (e.g., 0.5 samples per one person, 1 sample per 1 person, etc.).
As per claim 11: Regarding the claim limitations below, Reference Chien in view of Reference Das shows:
wherein the estimation unit inputs, to the generative model, a prompt including instruction information for summarizing estimation results and instruction information for naming a title of obtained summary content, and the generative model outputs the title and the description of the cluster based on the instruction information.
(Reference Chien: [0059] For instance, the aggregating may comprise tallying the plurality of outputs associated with each of a plurality of dispositions from a defined set of dispositions. For example, the first disposition may comprise a disposition from the defined set of dispositions having a highest tally count (or from among the top three dispositions, the top four dispositions, etc.), a disposition having a score, tally, or the like above a threshold, and so forth. In other words, the first disposition may be identified as being associated with the zone (e.g., characteristic or representative of the zone) when the first disposition is has a higher tally count that for other dispositions. In one example, the first disposition may be identified as being associated with the first zone when a threshold number or percentage of the plurality of outputs comprises the first disposition. For example, the threshold number or percentage may be based upon a total number of the plurality of inputs. For instance, the processing system may establish that a minimum number of samples of sensor data/input data is required before any dispositions may be considered representative of a zone. In one example, the minimum number of samples may be fixed, or may be based upon a number of people estimated to be present in the zone. For instance, if the first zone is estimated to have 10,000 people, a minimum of 5,000 samples, 10,000 samples, etc. may be required (e.g., 0.5 samples per one person, 1 sample per 1 person, etc.).
Although Reference Chien discloses gathering information about an area from users and adding context to the data ([0028], [0035], and [0051]), Reference Chien also shows machine learning models and neural networks in [0027], [0035], [0037], [0043], [0054], [0064], however, Reference Chien does not disclose “generative model trained”. Reference Das discloses “generative model trained” (Col. 5, lines 12-27: The LLM 110 may be a language model (e.g., a generative artificial intelligence (AI) model, a generative adversarial network (GAN) model, a generative pre-trained transformer (GPT) model) including an artificial neural network (ANN) with a set of parameters (e.g., tens of weight, hundreds of weights, thousands of weights, millions of weights, billions of weights, trillions of weights), initially trained on a quantity of unlabeled content (e.g., text, unstructured text, descriptive text, imagery, sounds) using a self-supervised learning algorithm or a semi-supervised learning algorithm to understand a set of corresponding data relationships. Then, the LLM 110 may be further trained by fine-tuning or refining the set of corresponding data relationships via a supervised learning algorithm or a reinforcement learning algorithm. Reference Das also shows summarization col. 8, lines 42-60: The logic 112 may be built via or on top of a software development language integration framework (e.g., LangChain framework) designed to simplify at least some creation of applications using LLMs, including the LLM 110 (e.g., to be data aware or agentic). The computing instance 108 may host the software development language integration framework or the software development language integration framework may be hosted off the computing instance 108 (e.g., a server). The software development language integration framework can be used for chatbots, Generative Question-Answering (GQA), summarization, and other techniques suitable for algorithms disclosed herein, by chaining together different components to create more advanced use cases around the LLM 110. Although the LLM 110 and the logic 112 are separate and distinct from each other, which may allow the logic 112 to programmatically engage with the LLM 110 on-demand, this configuration is not required and the LLM 112 can include the logic 112 or the logic 112 can include the LLM 110. Col. 9, lines 19-40: In situations where at least more precision may be needed, the logic 112 may include or interface with an AI agent (e.g., Auto-GPT agent) that, given a goal in natural language (e.g., human-readable unstructured or descriptive text), attempts to reach the goal by breaking the goal into a set of sub-tasks and using the network 102 (e.g., Internet) and other tools in an automatic looping process. For example, the AI agent may assigns itself new objectives to work on with a focus on achieving a greater goal, without manual input, by executing responses to prompts to accomplish a goal task, and in doing so will create and revise its own prompts to recursive instances in response to new information. The AI agent may manage its short-term or long-term memory by writing to and reading from databases and files, manage context window length requirements with summarization, may perform network-based actions (e.g., unattended web searching interactions, unattended web form interactions, unattended API interactions), or others. For example, the logic 112 can invoke or utilize LangChain framework and Llama toolkit, together with the AI agent. The computing instance 108 may host the AI agent or the AI agent may be hosted off the computing instance 108.
Reference Chien and Reference Das are analogous prior art to the claimed invention because the references generally relate to field of gathering large amounts of data and then using machine learning models analyzing it. Further, said references are part of the same classification, i.e., G06Q. Lastly, said references are filed before the effective filing date of the instant application; hence, said references are analogous prior-art references.
It would have been obvious to one of ordinary skill in the art before the effective filing date of this application for AIA to provide the teachings of Reference Das, particularly the ability to use generative model trained (Col. 5, lines 12-27), in the disclosure of Reference Chien, particularly in the gathering information about an area from users and adding context to the data ([0028], [0035], and [0051]), Reference Chien also shows machine learning models and neural networks in [0027], [0035], [0037], [0043], [0054], [0064], in order to provide for a system that uses generative models initially trained on a quantity of unlabeled content (e.g., text, unstructured text, descriptive text, imagery, sounds) using a self-supervised learning algorithm or a semi-supervised learning algorithm to understand a set of corresponding data relationships as taught by Reference Das (see at least in Col. 5, lines 12-27), where upon the execution of the method and system of Reference Das for improving functioning of computers by enabling large language models to review bytecodes executable on distributed ledgers to determine whether those bytecodes enable smart contracts on those distributed ledgers and then acting accordingly (Reference Das: col. 1, lines 40-47) so that the process of gathering large amounts of data and then using machine learning models analyzing it can be made more efficient and effective.
Further, the claimed invention is merely a combination of old elements in a similar gathering large amounts of data and then using machine learning models analyzing it field of endeavor, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that, given the existing technical ability to combine the elements as evidenced by Reference Chien in view of Reference Das, the results of the combination were predictable (MPEP 2143 A).
As per claim 12: Regarding the claim limitations below, Reference Chien in view of Reference Das shows:
wherein the acquisition unit further acquires facility information including a dwell time, opening hours, and a crowded condition for each place included in the predetermined area, and the estimation unit extracts the target area further based on the facility information.
(Reference Chien: [0059] For instance, the aggregating may comprise tallying the plurality of outputs associated with each of a plurality of dispositions from a defined set of dispositions. For example, the first disposition may comprise a disposition from the defined set of dispositions having a highest tally count (or from among the top three dispositions, the top four dispositions, etc.), a disposition having a score, tally, or the like above a threshold, and so forth. In other words, the first disposition may be identified as being associated with the zone (e.g., characteristic or representative of the zone) when the first disposition is has a higher tally count that for other dispositions. In one example, the first disposition may be identified as being associated with the first zone when a threshold number or percentage of the plurality of outputs comprises the first disposition. For example, the threshold number or percentage may be based upon a total number of the plurality of inputs. For instance, the processing system may establish that a minimum number of samples of sensor data/input data is required before any dispositions may be considered representative of a zone. In one example, the minimum number of samples may be fixed, or may be based upon a number of people estimated to be present in the zone. For instance, if the first zone is estimated to have 10,000 people, a minimum of 5,000 samples, 10,000 samples, etc. may be required (e.g., 0.5 samples per one person, 1 sample per 1 person, etc. [0028] In an illustrative example, server(s) 116 may generate a disposition profile of zone 1 in accordance with one or more dispositions of zone 1 determined based upon sensor data from various sensor devices, e.g., including at least one of camera 141 or microphone 143, and in one example further including AQS 146 and/or WQS 147, sensor data from sensors of UAV 160 and/or mobile sensor station 170, and so forth. For instance, camera 141 may collect image data (e.g., video and/or still images) which may appear to include a number of people gathered in a park playing baseball. In one example, server(s) 116 may apply detection models (e.g., MLMs or the like stored in DB(s) 118) for detecting semantic content in video, such as “baseball,” “exercise,” “crowd,” “car,” “traffic,” etc. In one example, the semantic content may be mapped to various dispositions from a defined set of dispositions. For instance, detected “sports” and “recreation” can be mapped to one or more dispositions of “vibrant,” “healthy,” etc. In another example, server(s) 116 may apply detection models for semantic content, where the semantic content comprises the dispositions from the defined set of dispositions. For instance, server(s) 116 may deploy detection models for dispositions of: “sensitive,” “proud,” “extroverted,” “friendly,” “curious,” “dour,” “content,” “restless,” “driven,” “relaxed,” “uptight,” and so forth. For example, in such case, the dispositions may be identified more directly from the captured image data using detection models, without intermediate determination of other types of semantic content and then mapping into associated dispositions. In one example, server(s) 116 may apply detection models for detecting human faces (and emotional states thereof) in image data from camera 141 or the like. In one example, the emotional states may comprise dispositions from the defined set of dispositions. In another example, the emotional states may comprise a larger set of emotional states that may be mapped to dispositions from the defined set of dispositions. [0065] In one example, the method 300 can include defining zones based upon landmarks, or accepting one or more inputs for user-defined neighborhoods or zones. For instance, city planners may use this results from step 350 for various purposes and may define a neighborhood as a zone for investigative purposes. In another instance, the results can be used to ascertain the mood of a large gathering of people to detect potential security or safety risks, e.g., the mood of a large crowd celebrating a sporting event outcome. However, in another example, this can automatically be done by the processing system to account for population density or perceived population density, a perceived basis for geographic grouping (or multiple factors indicative of geographic grouping), a number of available sensors in an area, etc. In one example, the method 300 may include obtaining and providing at step 350 additional data along with disposition information, such as a type of area (residential, commercial, office, industrial, recreational, etc.), an estimated density of people in the zone, e.g., based upon existing available census and demographic data or estimated in other ways, such as average number of unique detected mobile endpoint devices in the zone, and so forth. In one example, density of people may be broken down by morning, afternoon, evening, or hours of the day, days of the week, days, months, seasons or other times of the year, and so forth. In such an example, the method 300 may include obtaining a user/requester selection of a time period of interest for the reporting).
Response to Arguments
Applicants’ arguments are moot in view of the new grounds of rejection necessitated by the amendments made to previously presented claims.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
NPL Reference:
M. Imai, M. Enoki, R. Kudo and M. Oguchi, "Personalized Local Event Search Based on SNS Data Analysis," 2020 14th International Conference on Ubiquitous Information Management and Communication (IMCOM), Taichung, Taiwan, 2020, pp. 1-9, doi: 10.1109/IMCOM48794.2020.9001723.
This reference discloses since the Olympic Games were decided to be held in Tokyo in 2020, the number of foreign tourists visiting the city has been increasing rapidly. Accordingly, tourists have been seeking more sightseeing information. While guidebooks are good for pointing out popular tourist attractions, it is more difficult for tourists to get various types of detailed information that is suitable for each of them. We developed a tourist information distribution system that sends information corresponding to places and times. In addition to information on events that can be taken from existing travel books and the Web, the system enables users to get "event information" that is suitable for each of them in terms of hobbies by acquiring information on a wide variety of large and small events from a social networking service (SNS). In addition, we examined the preferences of foreigners visiting Japan to make the system more user-friendly. By estimating their preferences on the basis of social media streams and considering location information and time information, we aim to distribute event information that tourists can use on the spot at a particular time (Abstract).
Foreign Reference:
(JP 2022039743 A) Ishikawa et al. This reference discloses the apparatus (100) has an acquisition unit (131) that acquires multiple position information indicating each position of multiple users. An estimation unit (132) estimates the number of moving users who are moving users in an area based on the position information acquired by the acquisition unit. A calculation unit (133) calculates the temporal average of the moving users in the area based on the number of moving users estimated by the estimation unit. A providing unit (134) provides a user with information according to the average calculated by the calculating unit.
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
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/N.N.P/Examiner, Art Unit 3624
/PATRICIA H MUNSON/Supervisory Patent Examiner, Art Unit 3624
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