Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
DETAILED ACTION
The action is in response to claims dated 8/16/2022
Claims pending in the case: 1-20
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.
Claim(s) 1-20 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.
Step1: determine whether the claim is directed to one of the four statutory categories of invention, i.e., process, machine, manufacture, or composition of matter. If YES, proceed to Step 2A, broken into two prongs.
Step 2A, Prong 1: determine whether or not the claims recite a judicial exception (e.g., mathematical concepts, mental processes, certain methods of organizing human activity). If YES, the analysis proceeds to the second prong
Step 2A, Prong 2: determine whether or not the claims integrate the judicial exception into a practical application. If NOT, the analysis proceeds to determining whether the claim is a patent-eligible application of the exception (Step 2B).
Step 2B: If any element or combination of elements in the claim is sufficient to ensure that the claim integrates the judicial exception into a practical application, or else amounts to significantly more than the abstract idea itself.
Step 1 Analysis
According to the first part of the analysis, the instant case all claims are directed to one of the statutory categories of invention.
Step 2A Prong 1, Step 2A Prong 2, and Step 2B Analysis
Independent Claim 1 includes the following recitation of an abstract idea:
… output a recommendation output for a 4D data object associated with a target individual, the recommendation output being generated based on a comparison of a first set of features of the 4D data object and features of the plurality of 4D data objects from the knowledge base (Providing recommendation based on data comparison is practical to perform in the human mind under its broadest reasonable interpretation. This is a recitation of a mental process.);
Claim 1 recites the following additional elements, which, considered individually and as an ordered combination do not integrate the abstract idea into a practical application:
receiving a plurality of four-dimensional (4D) data objects, the plurality of 4D data objects including time-series three-dimensional (3D) models of individuals and annotations associated with the time-series 3D models, each time-series 3D model from the plurality of 4D objects including media content captured by a multi-camera system and combined into 3D models showing movement of an individual over a duration of time (This is insignificant extra-solution activity, which does not integrate the abstract idea into a practical application or amount to significantly more than the abstract idea. See MPEP 2106.05(g). Moreover, sending, receiving, storing and retrieving information is well-understood, routine, conventional as evidenced by the court cases cited at MPEP 2106.05(d), example i. Receiving or transmitting data and iv. Storing and retrieving information and MPEP 2106.05(g), example iv. Obtaining information about transactions using the Internet to verify credit card transactions);
generating a knowledge base of the plurality of 4D data objects, the knowledge base including an accessible storage of the plurality of 4D data objects (Generating a knowledge base is well known, the “generating” is recited at a high level of generality, the limitation therefore appears to represent extra-solution activity because it is a mere nominal or tangential addition to the claim, amounting to mere data gathering. This is insignificant extra-solution activity, which does not integrate the abstract idea into a practical application or amount to significantly more than the abstract idea. See MPEP 2106.05(g). Moreover, sending, receiving, storing and retrieving information is well-understood, routine, conventional as evidenced by the court cases cited at MPEP 2106.05(d), example i. Receiving or transmitting data and iv. Storing and retrieving information and MPEP 2106.05(g), example iv. Obtaining information about transactions using the Internet to verify credit card transactions) ; and
training a 4D recommendation model to output a recommendation output (This high level recitation of training of a model is a mere instruction to apply the judicial exception. It only appears to amount to the use of a generically recited, off the shelf component, as a tool to implement the process and is not an inventive concept. Since the model is used merely as a tool to implement an existing process, this does not integrate the abstract idea into a practical application or amount to significantly more than the abstract idea. See MPEP 2106.05(f).)
These claimed limitations therefore do not integrate the abstract idea into a practical application.
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. In this case, after considering all claim elements individually and as an ordered combination, it is determined that the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception for the reasons given above with respect to integration of the abstract idea into a practical application.
Therefore the claim is not patent eligible.
Independent Claims 11, includes the following recitation of an abstract idea:
identify features of a given 4D data object; compare the identified features to features of a knowledge base of 4D data objects to determine a subset of 4D data objects from the knowledge base having a threshold similarity to the given 4D data object; and output a recommendation associated with the subset of 4D data objects and based on comparing the identifiers features to features of 4D data objects from the knowledge base, the recommendation including a prediction associated with the given 4D data object (Providing recommendation based on data features and data comparison is practical to perform in the human mind under its broadest reasonable interpretation. This is a recitation of a mental process.);
Claim 11 recites the following additional elements, which, considered individually and as an ordered combination do not integrate the abstract idea into a practical application:
receiving an input four-dimensional (4D) data object including a time-series three-dimensional (3D) model of an individual and annotations associated with the individual, the time-series 3D model including media content captured by a multi-camera system and combined into 3D models showing movement of the individual over a duration of time (This is insignificant extra-solution activity, which does not integrate the abstract idea into a practical application or amount to significantly more than the abstract idea. See MPEP 2106.05(g). Moreover, sending, receiving, storing and retrieving information is well-understood, routine, conventional as evidenced by the court cases cited at MPEP 2106.05(d), example i. Receiving or transmitting data and iv. Storing and retrieving information and MPEP 2106.05(g), example iv. Obtaining information about transactions using the Internet to verify credit card transactions);
applying a 4D recommendation model to the input 4D data object to generate a recommendation output for the input 4D data object (This high level recitation of using a model is a mere instruction to apply the judicial exception. It only appears to amount to the use of a generically recited, off the shelf component, as a tool to implement the process and is not an inventive concept. Since the model is used merely as a tool to implement an existing process, this does not integrate the abstract idea into a practical application or amount to significantly more than the abstract idea. See MPEP 2106.05(f).),
causing a presentation of the recommendation output to be displayed via a graphical user interface of a client device (This is a recitation of generic computer components to be used in performing the abstract idea, which does not integrate the abstract idea into a practical application or amount to significantly more than the abstract idea. See MPEP 2106.05(f).).
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. In this case, after considering all claim elements individually and as an ordered combination, it is determined that the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception for the reasons given above with respect to integration of the abstract idea into a practical application.
Therefore the claim is not patent eligible.
Independent Claims 19, is similar in scope as claim 1 and therefore rejected under the same rationale. The additional elements of “at least one processor; memory in electronic communication with the at least one processor; and instructions stored in the memory” also do not integrate the abstract idea into a practical application or amount to significantly more than the abstract idea (This is a recitation of generic computer components to be used in performing the abstract idea, which does not integrate the abstract idea into a practical application or amount to significantly more than the abstract idea. See MPEP 2106.05(f).).
The dependent claims recite at least the abstract idea identified above in the claim upon which it depends and recites the following additional elements which, considered individually and as an ordered combination with the additional elements from the claim upon which it depends, do not integrate the abstract idea into a practical application or amount to significantly more than the abstract idea.
Dependent claim 2-4, 6-7, 17-18 pertain to type of input and output data.
Dependent claim 5, 9, 12-15, 20 pertain to determining similarity (This is practical to perform in the human mind under its broadest reasonable interpretation. This is a further recitation of a mental process.).
Dependent claim 8, 16 pertain to data gathering (This is insignificant extra-solution activity, which does not integrate the abstract idea into a practical application or amount to significantly more than the abstract idea. See MPEP 2106.05(g). Moreover, sending, receiving, storing and retrieving information is well-understood, routine, conventional as evidenced by the court cases cited at MPEP 2106.05(d), example i. Receiving or transmitting data and iv. Storing and retrieving information and MPEP 2106.05(g), example iv. Obtaining information about transactions using the Internet to verify credit card transactions).
Dependent claim 10 pertain to the hardware being used (This is a recitation of generic computer components to be used in performing the abstract idea, which does not integrate the abstract idea into a practical application or amount to significantly more than the abstract idea. See MPEP 2106.05(f).)
The dependent claims therefore, do not integrate the abstract idea into a practical application or amount to significantly more than the abstract idea
Hence these claims are rejected as being abstract.
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) 1-11, 13, 15-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Franz (US 20160267327) in view of Khokhlova (Advances in description of 3D human motion – henceforth referred to as Khok) and He (US 20210027878).
Regarding Claim 1, Franz teaches, a method, comprising:
receiving a plurality of four-dimensional (4D) data objects, the plurality of 4D data objects including time-series three-dimensional (3D) models of individuals and annotations associated with the time-series 3D models, each time-series 3D model from the plurality of 4D objects including media content captured by a multi-camera system and combined into 3D models showing movement of an individual over a duration of time (Franz: [11, 13, 16, 25, 45, 54-55, 69]: [13]: “three-dimensional images are recorded … a four-dimensional information structure will correspondingly be generated”; [25]: " a motion profile of the particular object can thus also be used for the detection and the corresponding predefinition" [45]: " detect a rhythm in the motion of this object in the form of a limb");
… to output a recommendation output for a 4D data object associated with a target individual (Franz: [45, 51, 53-54]: alarm if a seizure, fall etc. detected),
However, Franz does not specifically teach,
generating a knowledge base of the plurality of 4D data objects, the knowledge base including an accessible storage of the plurality of 4D data objects; and
training a 4D recommendation model;
the recommendation output being generated based on a comparison of a first set of features of the 4D data object and features of the plurality of 4D data objects from the knowledge base;
Khok teaches,
generating a knowledge base of the plurality of 4D data objects, the knowledge base including an accessible storage of the plurality of 4D data objects (Khok: Pg. 7 section 2.2 [1-2]: “A common database … used for designing, training and evaluating… construction and annotation of a new database”; Pg. 9 [1]: Human action database);
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of Frenz and Khok because the combination would enable using a knowledgebase of 4D data to generate recommendation. One of ordinary skill in the art would have been motivated to combine the teachings because the combination would enable using exiting information to improve recommendations.
He further teaches,
training a recommendation model (He: [23, 30, 36]: database with information and annotations; [27]: change with time (4D info); [42-43, 48, 52, 63]: trained NN for recommendation);
the recommendation output being generated based on a comparison of a first set of features of the data object and features of the plurality of 4D data objects from the knowledge base (He: [24, 39, 46]: display recommendation based on comparison with database information);
Although He does not specifically use 4D data, Khokhlova specifically recites that such 4D databased may be used for training purpose and Franz teaches providing recommendation based on 4D data. It would have been obvious to one skilled in the art that a recommendation model may be used with the 4D database to generate recommendations.
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of Franz, Khok and He because the combination would enable training and using a recommendation model to generate recommendations. One of ordinary skill in the art would have been motivated to combine the teachings because the combination would improve efficiency in determining a recommended plan by using machine learning models.
Regarding claim 2, Franz, Khok and He teach the invention as claimed in claim 1 above and, wherein the annotations associated with the time-series 3D models include text associated with individuals depicted by the time-series 3D models (He: [23, 30]: annotations with patent condition).
Regarding claim 3, Franz, Khok and He teach the invention as claimed in claim 1 above and, wherein the annotations associated with the time-series 3D models include demographic data associated with the individuals (He: [23, 27]: demographic information for comparison; [27] “include image features of a current CT or MR image but also include other potentially relevant information such as patient demographic”).
Regarding claim 4, Franz, Khok and He teach the invention as claimed in claim 1 above and, wherein the annotations associated with the time-series 3D models include human-generated recommendations determined by a healthcare provider and included within one of the plurality of 4D data objects (He: [23, 27, 30]: annotations with patent condition) (Khok: Pg. 7 section 2.2 [1-2]: 4D objects with annotation).
Regarding claim 5, Franz, Khok and He teach the invention as claimed in claim 1 above and, wherein the recommendation output includes a predicted recommendation for the target individual based on similarities between features of the 4D data object and a subset of 4D data objects from the plurality of 4D data objects having a shared set of features as the 4D data object (Khok: Pg. 7 section 2.2 [1-2]: 4D objects with annotation) (He: [24, 27, 39, 46]: recommendation based on comparison with database information which may be changes with time (4D info)).
Regarding claim 6, Franz, Khok and He teach the invention as claimed in claim 1 above and, wherein the recommendation output includes a predicted diagnosis of a health condition of the target individual based on the comparison of the first set of features and features of the plurality of 4D data objects (He: [24, 27, 39, 46]: recommendation based on comparison with database information).
Regarding claim 7, Franz, Khok and He teach the invention as claimed in claim 1 above and, wherein the recommendation output includes a predicted recovery status of a health condition based on the comparison of the first set of features and features of the plurality of 4D data objects (He: [24, 27, 39, 46, 63]: recommendation based on comparison with database information predicting a plan for patient recovery (status) ).
Regarding claim 8, Franz, Khok and He teach the invention as claimed in claim 1 above and, wherein the recommendation output includes an identification of a gesture to be performed by the target individual to collect additional information to include within the 4D data object (He: [25, 34, 38, 40-41]: recommendation system collects additional info).
Regarding claim 9, Franz, Khok and He teach the invention as claimed in claim 8 above and, wherein the comparison of features includes a comparison of features from the plurality of 4D data objects of the knowledge base and additional media content captured and included within the 4D data object based on performance of the gesture by the target individual (He: [25, 34, 38, 40-41]: recommendation system updated to use additional content that is capture during treatment);
Regarding claim 10, Franz, Khok and He teach the invention as claimed in claim 1 above and, wherein the multi-camera system includes plurality of depth capable cameras oriented around a central position and calibrated to capture media content depicting the individual over the duration of time (Franz: [6, 11]: depth camera device for monitoring).
Regarding claim 11, Franz teaches, a method, comprising:
receiving an input four-dimensional (4D) data object including a time-series three-dimensional (3D) model of an individual and annotations associated with the individual, the time-series 3D model including media content captured by a multi-camera system and combined into 3D models showing movement of the individual over a duration of time (Franz: [11, 13, 16, 25, 45, 54-55, 69]: [13]: “three-dimensional images are recorded … a four-dimensional information structure will correspondingly be generated”; [25]: " a motion profile of the particular object can thus also be used for the detection and the corresponding predefinition" [45]: " detect a rhythm in the motion of this object in the form of a limb");
applying … the input 4D data object to generate a recommendation output for the input 4D data object (Franz: [45, 51, 53-54]: alarm if a seizure, fall etc. detected), the 4D recommendation model being configured to:
identify features of a given 4D data object (Franz: [45, 51, 53-54]: identify feature to determine a seizure, fall etc.);
…
output a recommendation associated with the subset of 4D data objects … (Franz: [45, 51, 53-54]: alarm recommending attention if a seizure, fall etc. is detected); and …
However, Franz does not specifically teach,
a 4D recommendation model;
compare the identified features to features of a knowledge base of 4D data objects to determine a subset of 4D data objects from the knowledge base having a threshold similarity to the given 4D data object;
… based on comparing the identifiers features to features of 4D data objects from the knowledge base, the recommendation including a prediction associated with the given 4D data object;
causing a presentation of the recommendation output to be displayed via a graphical user interface of a client device;
Khok teaches,
a knowledge base of 4D data objects (Khok: Pg. 7 section 2.2 [1-2]: “A common database … used for designing, training and evaluating… construction and annotation of a new database”; Pg. 9 [1]: Human action database);
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of Frenz and Khok because the combination would enable using a knowledgebase of 4D data to generate recommendation. One of ordinary skill in the art would have been motivated to combine the teachings because the combination would enable using exiting information to improve recommendations;
He further teaches,
compare the identified features to features of a knowledge base of 4D data objects to determine a subset of 4D data objects from the knowledge base having a threshold similarity to the given 4D data object (He: [24, 39, 46]: display recommendation based on comparison with database information) If is obvious that a comparison criteria (threshold) is being used to determine similarity;
… based on comparing the identifiers features to features of 4D data objects from the knowledge base, the recommendation including a prediction associated with the given 4D data object (He: [24, 39, 46]: display recommendation based on comparison with database information; [82]: disease progression (prediction));
causing a presentation of the recommendation output to be displayed via a graphical user interface of a client device (He: [24, 39, 46]: display recommendation);
Although He does not specifically use 4D data, Khokhlova specifically recites that such 4D databased may be used for training purpose and Franz teaches providing recommendation based on 4D data. It would have been obvious to one skilled in the art that a recommendation model may be used with the 4D database to generate recommendations.
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of Franz, Khok and He because the combination would enable training and using a recommendation model to generate recommendations. One of ordinary skill in the art would have been motivated to combine the teachings because the combination would improve efficiency in determining a recommended plan by using machine learning models.
Regarding claim 13, Franz, Khok and He teach the invention as claimed in claim 11 above and, wherein the threshold similarity includes a threshold similarity between text from annotations of the given 4D data object and text of annotations from the subset of 4D data objects (He: [24-25, 39, 46]: recommendation based on comparison of features and anotations with database information).
Regarding claim 15, Franz, Khok and He teach the invention as claimed in claim 11 above and, further comprising receiving a user input identifying a subset of features of the input 4D data object, wherein the recommendation output is determined based on a comparison between the input 4D data object and a subset of 4D data objects from the knowledge base that share the identified subset of features (He: [24, 39, 46]: recommendation based on comparison of features with database information).
Regarding Claim(s) 16, this/these claim(s) is/are similar in scope as claim(s) 9. Therefore, this/these claim(s) is/are rejected under the same rationale.
Regarding claim 17, Franz, Khok and He teach the invention as claimed in claim 16 above and, further comprising applying the 4D recommendation model to the updated version of the input 4D data object to generate the recommendation output for the updated version of the input 4D data object, the recommendation output being based on the additional information included within the input 4D data object (He: [25, 34, 38, 40-41]: recommendation system updated to use additional content that is capture during treatment) (Franz: [11, 13, 16, 25, 45, 54-55, 69]: analyzing 4D data objects).
Regarding Claim(s) 18, this/these claim(s) is/are similar in scope as claim(s) 6 and 7 combined. Therefore, this/these claim(s) is/are rejected under the same rationale.
Regarding Claim(s) 19-20, this/these claim(s) is/are similar in scope as claim(s) 1. Therefore, this/these claim(s) is/are rejected under the same rationale.
Claim(s) 12, 14 is/are rejected under 35 U.S.C. 103 as being unpatentable over Franz (US 20160267327) in view of Khokhlova (Advances in description of 3D human motion – henceforth referred to as Khok) and He (US 20210027878) in further view of Navani (US 20220261917).
Regarding claim 12, Franz, Khok and He teach the invention as claimed in claim 11 above and, wherein the threshold similarity includes …shared features between the subset of 4D data objects and the identified features of the given 4D data object (He: [24, 39, 46]: recommendation based on comparison of identified features with those in database);
But not, a threshold number of shared features;
Navani teaches, a threshold number of shared features (Navani: [95]: a threshold number of similar features may be used to generate recommendation);
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of Franz, Khok, He and Navani because the combination would enable training and using a threshold number of feature to identify similarity as is common in the art.
Regarding claim 14, Franz, Khok and He teach the invention as claimed in claim 11 above and, wherein the threshold similarity includes .. similar demographic features between the given 4D data object and individuals associated with the subset of 4D data objects (He: [23, 27]: demographic information for comparison; [27] “include image features of a current CT or MR image but also include other potentially relevant information such as patient demographic”) (Franz: [11, 13, 16, 25, 45, 54-55, 69]: analyzing 4D data objects) ;
Navani further teaches, Navani teaches, a threshold number of similar features (Navani: [95]: a threshold number of similar features may be used to generate recommendation); It is obvious that a threshold number may be used for any parameter being used to determine a similarity;
The same motivation to combine stated above applies.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure in attached 892.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MANDRITA BRAHMACHARI whose telephone number is (571)272-9735. The examiner can normally be reached Monday to Friday, 11 am to 8 pm EST.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Tamara Kyle can be reached at 571 272 4241. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/Mandrita Brahmachari/Primary Examiner, Art Unit 2144