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 .
Application Status
Present office action is in response to application filed 11/22/2024. Claims 1-19 are currently pending in the application.
Drawings
The drawings are objected to because each of figures 1-3 and 5 includes software screenshots with excessive shading that obscures the text of the drawings making them difficult to understand and reproduce. The photographs must be of sufficient quality so that all details in the photographs are reproducible in the printed patent as required under MPEP §608.01(f) and CFR 1.84.
Corrected drawing sheets in compliance with 37 CFR 1.121(d) are required in reply to the Office action to avoid abandonment of the application. Any amended replacement drawing sheet should include all of the figures appearing on the immediate prior version of the sheet, even if only one figure is being amended. The figure or figure number of an amended drawing should not be labeled as "amended." If a drawing figure is to be canceled, the appropriate figure must be removed from the replacement sheet, and where necessary, the remaining figures must be renumbered and appropriate changes made to the brief description of the several views of the drawings for consistency. Additional replacement sheets may be necessary to show the renumbering of the remaining figures. Each drawing sheet submitted after the filing date of an application must be labeled in the top margin as either "Replacement Sheet" or "New Sheet" pursuant to 37 CFR 1.121(d). If the changes are not accepted by the examiner, the applicant will be notified and informed of any required corrective action in the next Office action. The objection to the drawings will not be held in abeyance.
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
Use of the word “means” (or “step for”) in a claim with functional language creates a rebuttable presumption that the claim element is to be treated in accordance with 35 U.S.C. 112(f) (pre-AIA 35 U.S.C. 112, sixth paragraph). The presumption that 35 U.S.C. 112(f) (pre-AIA 35 U.S.C. 112, sixth paragraph) is invoked is rebutted when the function is recited with sufficient structure, material, or acts within the claim itself to entirely perform the recited function.
Absence of the word “means” (or “step for”) in a claim creates a rebuttable presumption that the claim element is not to be treated in accordance with 35 U.S.C. 112(f) (pre-AIA 35 U.S.C. 112, sixth paragraph). The presumption that 35 U.S.C. 112(f) (pre-AIA 35 U.S.C. 112, sixth paragraph) is not invoked is rebutted when the claim element recites function but fails to recite sufficiently definite structure, material or acts to perform that function.
Claim elements in this application that use the word “means” (or “step for”) are presumed to invoke 35 U.S.C. 112(f) except as otherwise indicated in an Office action. Similarly, claim elements that do not use the word “means” (or “step for”) are presumed not to invoke 35 U.S.C. 112(f) except as otherwise indicated in an Office action.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder (unit) that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitations are: “data ingestion module” in claims 13 and 19, “machine learning model training module” in claims 13 and 17, “prediction module” in claim 13, “recommendation module” in claim 18. This interpretation is based off of the language “module” as consistent with MPEP §2181(I)(A).
Because these claim limitations are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, they are being interpreted to cover the corresponding structure/algorithm described in the specification as performing the claimed function, and equivalents thereof. In particular, the originally filed specification discloses: ¶ 24: a prediction module operable to generate predictive outcomes based on real-time input data using the trained machine learning models and a user interface enabling users to input data and view predictions; ¶ 26: the data ingestion module of the multiple intelligences recognition system is a digital camera; ¶ 28: the machine learning model training module, and the prediction module of the multiple intelligences recognition system is controlled by a computer program; ¶ 73: … the feature extraction module, the machine learning model training module, and the prediction module of the multiple intelligences recognition system is controlled by a computer program; ¶ 78: …. The MIRA System of the present invention then proposes activities to enhance a student's linguistic, musical, naturalistic, interpersonal or intrapersonal multiple intelligences; ¶ 180: …. This proposal of multiple intelligence activities is the recommendation module of the MIRA System he Recommendation Module of the MIRA System provides an activity analysis for each student and determines if there is a deficiency in a specific type of activity.
If applicant wishes to provide further explanation or dispute the examiner’s interpretation of the corresponding structure, applicant must identify the corresponding structure with reference to the specification by page and line number, and to the drawing, if any, by reference characters in response to this Office action.
If applicant does not intend to have the claim limitation(s) treated under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may amend the claim(s) so that it/they will clearly not invoke 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, or present a sufficient showing that the claim recites/recite sufficient structure, material, or acts for performing the claimed function to preclude application of 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
Claim Objections
Claims 1-12 and 16 are objected to because of the following informalities: Claim 1, line 3 should recite “the multiple intelligences recognition system” to establish antecedent basis and avoid claim ambiguity with the earlier recitation of “a multiple intelligences recognition system” in the preamble of the claim. The claim will be interpreted as such for purpose of examination.
Claim 16, lines 1-2 should recite “one of the multiple intelligences” to establish antecedent basis and avoid claim ambiguity with the earlier recitation of “multiple intelligences” in the preamble of the claim. The claim will be interpreted as such for purpose of examination.
Appropriate correction is required.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claim 12 is rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention. In claim 12, there is a lack of antecedent basis for the recitation of “… continuously improve the machine learning models …”. In particular, “machine learning models” was not previously set forth in the claim. In view of the foregoing, the metes and bounds of the claim cannot be discerned.
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 1-19 are rejected under 35 U.S.C. 101 because the claimed invention is directed to abstract idea without significantly more.
Step 1: Statutory Category?
Independent claims 1 and 13 each recites “a multiple intelligences recognition system” (i.e. a machine). As such, independent claims 1 and 16 are each directed to a statutory category of invention within § 101, i.e., machine. (Step 1: YES).
Step 2A – Prong 1: Judicial Exception Recited?
Independent claim 1, analyzed as representative of the claimed subject matter, is reproduced below. The limitations determined to be abstract ideas are shown in italics. The additional element(s) recited at a high level of generality are shown in bold. The limitation(s) determined to be extra-solution activity are underlined.
A multiple intelligences recognition system comprising
[L1] a digital camera and computer program, wherein the digital camera captures photos and videos and
[L2] stores said photos and videos in the multiple intelligences recognition system,
[L3] wherein the multiple intelligences recognition system analyzes the captured photos and videos into types of multiple intelligences and
[L4] recommends at least one activity for each deficient multiple intelligence identified.
The originally filed Specification discloses “[T]he present invention is related to education technology (learning method, multiple intelligence) for improving cognitive skills in the classroom” (¶ 1) and that “Dr. Howard Gardener proposed that these different types of multiple intelligence were: visual-spatial, linguistic-verbal, logical-mathematical, body-kinesthetic, musical, interpersonal, intrapersonal and naturalistic. Individuals draw on these multiple intelligences, separately or in combination, to solve problems (Gardner et al., 2011)” (¶ 2). It is apparent that humans have long used pen and paper to recognize and support multiple intelligences. Thus, other than reciting a “multiple intelligences recognition system” “digital camera” and “digital camera”, under the broadest reasonable interpretation, at least the italicized claim limitations may be performed using pen and paper, in the human mind, including observations, evaluations, and judgments and may also be characterized as a certain method of organizing human activity, i.e., managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions). Accordingly, the claim recites an abstract idea under Step 2A: Prong 1. (Step 2A – Prong 1: YES).
Step 2A – Prong 2: Integrated into a Practical Application?
The “multiple intelligences recognition system” and “digital camera”, are recited at a high level of generality (see published Specification (at least ¶ 10: … the storybook or gamebook of the disclosed invention is digital or experienced through a computer or similar device. In such embodiments, the pages, illustrations, text, or other symbolic representations of the storybook or gamebooks are read and experienced digitally. The reader or user can shuffle or rearrange the pages by a click of a button or by independently arranging them as they desire. In some embodiments, the computer may read the stories out loud to the user. In further embodiments, an animation can be created and displayed to act out the storyline that the user creates…; ¶ 30: The storybook assembly 100 may comprise a first page 102, any number of story pages 106, and a last page, 104 as seen in FIGS. 1-3. The storybook assembly 100 may also comprise a receptacle 108. The receptacle 108 is a storage device for the pages when they are not in use. The receptacle 108 may be a box or sleeve, and may be paper, plastic, or another suitable material for its use. The receptacle may have text 110 or illustrations 112, as depicted in FIG. 4. The text 100 and illustration 112 may match the theme or general storyline depicted in the story pages 106 within the storybook assembly 100 …; ¶ 30: The storybook assembly 100 may comprise a first page 102, any number of story pages 106, and a last page, 104 as seen in FIGS. 1-3. The storybook assembly 100 may also comprise a receptacle 108. The receptacle 108 is a storage device for the pages when they are not in use. The receptacle 108 may be a box or sleeve, and may be paper, plastic, or another suitable material for its use. The receptacle may have text 110 or illustrations 112, as depicted in FIG. 4. The text 100 and illustration 112 may match the theme or general storyline depicted in the story pages 106 within the storybook assembly 100…; ¶ 34: the storybook assembly 100 may have any number of story pages 106 with text 110 and illustrations 112. The story pages 106 may be arranged in any order selected by a user, and can be stacked, laid on a surface, or held in the user's hand. The user may begin the creation of their story by first selecting the first page 102, and then a sequence of story pages 106, concluding with the last page 104. Leaving the first page 102, and the last page 104 in their respective positions, the user may then continue to rearrange or shuffle the story pages 106 to read as a new story each time …. The lack of details about the “multiple intelligences recognition system” “digital camera” and “digital camera” indicates that the additional element(s) is/are generic, or part of generic computer elements performing or being used in performing the generic functions claimed. The additional elements [L1]: “receiving a textual representation based on verbal communication” (data gathering) and [L4]: “automatically providing an alert to at least one of the one or more users” (data transmission and/or data presentation) simply add insignificant extra-solution activity to the judicial exception, i.e., mere data gathering, data transmission and/or data presentation. Each of data gathering, data transmission and/or data presentation is generic and conventional. The claim limitations do not purport to improve the functioning of the “multiple intelligences recognition system” “digital camera” and “digital camera”, do not improve the technology of the technical field, and do not require a “particular machine.” Rather, they are performed using generic components. Further, the claim fails to effect any particular transformation of an article to a different state. The recited steps in the claim fail to provide meaningful limitations to limit the judicial exception. In this case, the claim merely uses the claimed computer elements as a tool to perform the abstract idea.
Considering the elements of the claim both individually and as “an ordered combination” the functions implemented by the “multiple intelligences recognition system” and “digital camera” at each step of the method are purely conventional. Each step performed in the claim does no more than require a generic computer/storybook assembly to perform a generic computer function. Thus, the claimed elements have not been shown to integrate the judicial exception into a practical application as set forth in the Revised Guidance which references the Manual of Patent Examining Procedure (“MPEP”) §§ 2106.04(d) and 2106.05(a)–(c) and (e)–(h). Because the abstract idea is not integrated into a practical application, the claim is directed to the judicial exception. (Step 2A, Prong Two: NO).
Step 2B: Claim provides an Inventive Concept?
As discussed with respect to Step 2A Prong Two, the “multiple intelligences recognition system” and “digital camera” in the claim amounts to no more than mere instructions to apply the exception using generic components. The same analysis applies here in Step 2B, i.e., mere instructions to apply an exception using generic computer components cannot integrate a judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B. Because the published Specification, as noted above (for example, (¶¶ 10. 30, 34)) describes the “multiple intelligences recognition system” and “digital camera” in general terms, without describing the particulars, the claim limitations may be broadly but reasonably construed as reciting conventional components and techniques, particularly in light of the published Specification sufficiently well-known that the specification does not need to describe the particulars of such additional element(s) to satisfy 35 U.S.C. § 112(a). See MPEP 2106.05(d), as modified by the USPTO Berkheimer Memorandum. Furthermore, the Berkheimer Memorandum, Section III (A)(1) explains that a specification that describes additional element(s) “in a manner that indicates that the additional element(s) is/are sufficiently well-known that the specification does not need to describe the particulars of such additional element(s) to satisfy 35 U.S.C. § 112(a)” can show that the elements are well understood, routine, and conventional); Intellectual Ventures I LLC v. Erie Indem. Co., 850 F.3d 1315, 1331 (Fed. Cir. 2017) (“The claimed mobile interface is so lacking in implementation details that it amounts to merely a generic component (software, hardware, or firmware) that permits the performance of the abstract idea, i.e., to retrieve the user-specific resources.”. The generic description of the “multiple intelligences recognition system” and “digital camera” indicates the claim steps are well-known enough that no further description is required for a skilled artisan to understand the process and that the implied component is used in a manner that is well-understood, routine, and conventional in the field. In particular, the recited data gathering steps [L1]: “receiving a textual representation based on verbal communication” (data gathering) and [L4]: “automatically providing an alert to at least one of the one or more users” (data transmission and/or data presentation) amount to nothing more than well-understood, routine, and conventional activity because these limitations are not distinguished from generic, conventional data gathering with a computer. As explained by the Federal Circuit in buySAFE, “[t]hat a computer receives and sends the information over a network—with no further specification—is not even arguably inventive.” 765 F.3d at 1355.
Considered as an ordered combination, the computer components of representative independent claim 1 add nothing that is not already present when the steps are considered separately. The sequence of the steps is equally generic and conventional. See Inventor Holdings, LLC v. Bed Bath & Beyond, Inc., 876 F.3d 1372, 1378 (Fed. Cir. 2017) (sequence of data retrieval, analysis, modification, generation, display, and transmission). Hence, the “multiple intelligences recognition system” and “digital camera” is/are generic, well-known, and conventional computing element(s). The use of the additional element(s) either alone or in combination amounts to no more than mere instructions to apply the judicial exception using generic computer component(s). Mere instructions to apply an exception using generic computer components cannot provide an inventive concept, and thus the claims are patent ineligible. (Step 2B: NO).
In regard to independent Claim 1:
Independent claim 1 recites multiple intelligences recognition system comprising components performing steps comparable to those of representative claim 13. Accordingly, independent claim 1 is rejected for reasons similar to those previously explained when addressing representative claim 13.
In regard to the dependent claims:
Dependents claims 2-12 and 14-19 include all the limitations of corresponding independent claims 1 and 13 from which they depend and, as such, recite the same abstract idea(s) noted above for corresponding independent claims 1 and 13. Each additional claim element, for example, “machine learning models” (claim 12), “digital camera” (claim 14), “mobile digital device” (claim 14) is recited as a generic computer component used according to its conventional purpose in a conventional manner. The Examiner fails to see any claim activity used in some unconventional manner nor does any produce some unexpected result. An invocation to use known technology in the manner it is intended to be used for its ordinary purpose is both generic and conventional. As per MPEP §§ 2106.05(a)–(c), (e)–(h), none of the limitations of claims 2-12 and 14-19 integrates the judicial exception into a practical application. Additionally, while dependent claims 2-12 and 14-19 may have a narrower scope than corresponding independent claims 1 and 18, no claim contains an “inventive concept” that transforms the corresponding claim into a patent-eligible application of the otherwise ineligible abstract idea(s). Therefore, dependent claims 2-12 and 14-19 are not drawn to patent eligible subject matter as they are directed to (an) abstract idea(s) without significantly more.
Rejections - 35 USC § 102/103
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 the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
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) A patent may not be obtained though the invention is not identically disclosed or described as set forth in section 102 of this title, if the differences between the subject matter sought to be patented and the prior art are such that the subject matter as a whole would have been obvious at the time the invention was made to a person having ordinary skill in the art to which said subject matter pertains. Patentability shall not be negatived by the manner in which the invention was made.
Claims 1-3, 13-16 and 19 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by or, in the alternative, under 35 U.S.C. 103 as obvious over Jaggers et al. (US 20210398054 A1) (Jaggers).
Re claims 1 and 13:
[Claim 1] Jaggers discloses a multiple intelligences recognition system comprising a digital camera and computer program, wherein the digital camera captures photos and videos and stores said photos and videos in a multiple intelligences recognition system (at least ¶ 39: collected information can comprise written material, images, videos, and/or sound capture of information), wherein the multiple intelligences recognition system analyzes the captured photos and videos into types of multiple intelligences (at least ¶ 42: images and/or video provided by at the ordering step can be analyzed by image processing methodologies and/or transcription of videos) and recommends at least one activity for each deficient multiple intelligence identified (at least ¶ 64: a camera can be configured to acquire photos or videos of the customer location, the system can be configured to automatically identify additional machines, devices, or systems present at the location. The camera can be included in a mobile device held by the technician, or the camera can be a separate device worn by the technician while on duty …; ¶ 71: deliver not only the type of training that a technician may need at a particular time, but … also allow such delivered training material to be aligned with a learning style of the technician).
Alternatively, in the event Jaggers is viewed as disclosing all the claim limitations but the claim limitations are viewed as not being part of a single embodiment and/or the claim elements are not disclosed explicitly as claimed, it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the invention to have modified Jaggers as claimed, because a person of ordinary skill has good reason to pursue the known options within his or her grasp. If this leads to the anticipated success, it is likely the product not of innovation but of ordinary skill and common sense.
[Claim 13] Jaggers discloses a multiple intelligences recognition system comprising a data ingestion module configured to receive and preprocess data; a feature extraction module operable to extract relevant features from the preprocessed data; a machine learning model training module configured to train machine learning models on the extracted features (at least ¶ 2: generate condition state information for one or more machines, devices, or systems. The condition state information can be used to predict current condition state information for a specific machine, device, or system … The technician can generate videos or other collateral while at a service or repair event location, where such collateral can be included for use in a library of machine learning information for subsequent condition state determinations; ¶ 18: labeling of the photos or videos for use of the photos or videos to generate a machine, device, or system object library for use in machine learning processes; ¶ 21: The information associated with the first technician in the technician database can include information associated with a learning style of the first technician, and selection of the one or more training material can incorporate the learning style. Information associated with the selection of the one or more training materials and evaluation of the first technician can be included in machine learning processes associated with selection of training materials for other technicians or in a subsequent selection of training materials for the first technician; ¶ 40: Information presented to and collected by the technician at the customer location can be associated with a machine learning system); a prediction module operable to generate predictive outcomes based on real-time input data using the trained machine learning models; and a user interface enabling users to input data and view predictions (at least ¶ 59: context-based information that can be provided to the technician in real-time; ¶ 74: provide technicians with a “personal assistant” or mentor in real-time while he is at a customer location to which he has been dispatched; ¶ 108: training module can be configured to utilize machine learning to learn from previous training events, technician assessments, and technician ratings of the training material. In this regard, information generated from various aspects of the technician database querying, selection and delivery of one or more training materials to a technician in need of training from a library of training materials can be used to train these machine learning processes; ¶ 109: data analytics and context-specific heuristic dashboards to provide supervisors and system administrators with real-time predictive advice regarding the skills portfolio of their technicians and future learning behaviors of individual technicians, group of technicians, and the organization as a whole; ¶ 111: The training module can also be configured to predict training completion level based on the technician's activity compared to group averages. This may be based on machine learning techniques that aggregate past technician experience data and continually adjust when new data is received; ¶ 113: the collected information can also be used to generate service programs for the customer, to predict and schedule subsequent service calls for the customer, to collect and analyze reliability information for the machine, system or device and to suggest replacement thereof …; ¶ 115: Machine learning systems from which information can be extracted from images automatically will be based, at least in part, on the ability to identify objects that are present therein without human supervision. In this regard, the machine learning systems can provide a prediction of an identity for a specific element in an image, or an “object,” that can be resolved therefrom. A “prediction” is the process of substantially automatically assigning a name, category, descriptive value or the like for the one or more objects that may require at least one additional processing step in addition the prediction step whereby a generated object output can be associated with the relevant object(s) as occurring in the scene. Prediction may also refer to the act of assigning a class, assigning a labeling, labeling an object; ¶ 126: automatic review generated at a customer location can be facilitated by comparison of objects extracted from the images using known computer vision methodologies and by comparison of such extracted objects with a suitable object library. As would be appreciated, an “object library” (also known as a “training set”) means the collection of objects for which machine learning systems can be configured to predict from images information. Such objects can include any and all objects for which one or more machine learning algorithms have been trained to recognize or to differentiate).
Alternatively, in the event Jaggers is viewed as disclosing all the claim limitations but the claim limitations are viewed as not being part of a single embodiment and/or the claim elements are not disclosed explicitly as claimed, it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the invention to have modified Jaggers as claimed, because a person of ordinary skill has good reason to pursue the known options within his or her grasp. If this leads to the anticipated success, it is likely the product not of innovation but of ordinary skill and common sense.
Re claims 2-3, 14-16 and 19:
[Claims 2-3] Jaggers teaches or at least suggests wherein the multiple intelligence is selected from the group consisting of spatial, kinesthetic, logical, linguistic, musical, naturalistic, interpersonal and intrapersonal (at least ¶ 95: when the assessment module identifies that a skills gap exists with the technician, and that the learning style of the technician is appropriate for learning by video instruction, the training module can be configured to automatically select a video from a video library that includes information associated with addressing the technician's identified skills gap), wherein the spatial multiple intelligence is spatial perception, spatial visualization, spatial reasoning, pattern recognition, design, artistic expression, navigation or a combination thereof (at least ¶ 102: visualizations (e.g., the look and feel of the site, the look of the rewards, the information shown to the user about their progress or the progress of other users, etc. )).
[Claim 14] Jaggers teaches or at least suggests wherein the data ingestion module obtains data from a digital camera or a mobile digital device (at least ¶ 64: a camera can be configured to acquire photos or videos of the customer location, the system can be configured to automatically identify additional machines, devices, or systems present at the location. The camera can be included in a mobile device held by the technician, or the camera can be a separate device worn by the technician while on duty …).
[Claims 15-16] Jaggers teaches or at least suggests wherein the features of the feature extraction module are multiple intelligences (at least ¶ 15: … provide better training materials to technicians in accordance with their skill levels, their real-time needs, and their learning styles …), wherein the multiple intelligence is selected from the group consisting of spatial, kinesthetic, logical, linguistic, musical, naturalistic, interpersonal and intrapersonal (at least ¶ 95: when the assessment module identifies that a skills gap exists with the technician, and that the learning style of the technician is appropriate for learning by video instruction, the training module can be configured to automatically select a video from a video library that includes information associated with addressing the technician's identified skills gap).
[Claim 19] Jaggers teaches or at least suggests wherein the data ingestion module supports real-time data streaming for continuous data flow and predictive analytics (at least ¶ 15: … provide better training materials to technicians in accordance with their skill levels, their real-time needs, and their learning styles …; ¶ 99: continuous engagement provided by inclusion of gaming features; ¶ 109: data analytics and context-specific heuristic dashboards to provide supervisors and system administrators with real-time predictive advice regarding the skills portfolio of their technicians and future learning behaviors of individual technicians, group of technicians, and the organization as a whole; ¶ 59: context-based information that can be provided to the technician in real-time; ¶ 74: provide technicians with a “personal assistant” or mentor in real-time while he is at a customer location to which he has been dispatched).
Claims 4-10 are rejected under 35 U.S.C. 103 as obvious over Jaggers, as applied to claims 1 and 2 above, in view of common knowledge.
Re claims 4-10:
[Claim 4] Jaggers appears to be silent on wherein the kinesthetic multiple intelligence is body control, fine motor skills, physical expression, athletic ability, body language, kinesthetic learning or a combination thereof. However, it is common knowledge that, among other things, “A kinaesthetic learner is someone who needs to be actively engaged in their education They are ‘tactile’ learners who use movement, testing, trial and error and a non-traditional learning environment to retain and recall information”. A kinesthetic learner Enjoys opportunities to go on excursions or be outside the classroom”, “Likes to build things and work with their hands”1. Hence, it would have been prima facie obvious to one of ordinary skill in the art, before the effective filing date of the invention, to have modified Jaggers as claimed because a person of ordinary skill has good reason to pursue the known options within his or her grasp. If this leads to the anticipated success, it is likely the product not of innovation but of ordinary skill and common sense.
[Claim 5] Jaggers appears to be silent on wherein the logical multiple intelligence is logical reasoning, deductive reasoning, problem-solving, mathematical operations, data analysis or a combination thereof. However, it is common knowledge that, among other things, “Logical learners thrive on orderly and sequential processes”, “[T]hey are great at following the steps to solve a problem or conduct an experiment and remembering those steps the next time”, “[T]hey make decisions based on facts and reason, rarely accounting for emotion”2. Hence, it would have been prima facie obvious to one of ordinary skill in the art, before the effective filing date of the invention, to have modified Jaggers as claimed because a person of ordinary skill has good reason to pursue the known options within his or her grasp. If this leads to the anticipated success, it is likely the product not of innovation but of ordinary skill and common sense.
[Claim 6] Jaggers appears to be silent on wherein the linguistic multiple intelligence is verbal communication, written communication, reading comprehension, vocabulary, wordplay, storytelling or a combination thereof. However, it is common knowledge that, among other things, “[T]he linguistic learning style is for those who enjoy learning from language in any form. Wordplay, metaphors, analogies, and rhymes are effective ways for them to understand and remember a concept”, “[G]roup discussions, and role-plays are perfect for verbal learners. When learning a language”, “[T]his type of learning style corresponds to people who would rather read a book than watch a movie”3. Hence, it would have been prima facie obvious to one of ordinary skill in the art, before the effective filing date of the invention, to have modified Jaggers as claimed because a person of ordinary skill has good reason to pursue the known options within his or her grasp. If this leads to the anticipated success, it is likely the product not of innovation but of ordinary skill and common sense.
[Claim 7] Jaggers appears to be silent on wherein the musical multiple intelligence is musical perception, musical memory, instrumental proficiency, vocal proficiency, musical composition, music theory, musical performance or a combination thereof. However, it is common knowledge that, among other things, musical learners “have music or a song in their heads most of the time”, “may even say ordinary things in a singsong way”, “enjoy singing and chanting”, “tap and move to rhythm”4. Hence, it would have been prima facie obvious to one of ordinary skill in the art, before the effective filing date of the invention, to have modified Jaggers as claimed because a person of ordinary skill has good reason to pursue the known options within his or her grasp. If this leads to the anticipated success, it is likely the product not of innovation but of ordinary skill and common sense.
[Claim 8] Jaggers appears to be silent on wherein the naturalistic multiple intelligence is observational skills, ecological knowledge, environmental stewardship, outdoor skills, agricultural knowledge, geographical knowledge, environmental design, conservation biology or a combination thereof. However, it is common knowledge, among other things, that, “[S]ome of the characteristics of … students with naturalist intelligence include their: Physically/emotionally adverse to pollution; Intense interest in learning about nature; Dramatic enthusiasm when in contact with nature; Powers of observation in nature; Awareness of changes in weather”5. Hence, it would have been prima facie obvious to one of ordinary skill in the art, before the effective filing date of the invention, to have modified Jaggers as claimed because a person of ordinary skill has good reason to pursue the known options within his or her grasp. If this leads to the anticipated success, it is likely the product not of innovation but of ordinary skill and common sense.
[Claim 9] Jaggers appears to be silent on wherein the interpersonal multiple intelligence is communication, collaboration, leadership, networking, social awareness, conflict resolution, cultural competence or a combination thereof. However, it is common knowledge, among other things, that the interpersonal learning style indicates “a person's ability to interact with others” and that “the characteristics that define people with this learning style” can be categorized “into three defining groups: Social … Natural leaders … Courageous”6. Hence, it would have been prima facie obvious to one of ordinary skill in the art, before the effective filing date of the invention, to have modified Jaggers as claimed because a person of ordinary skill has good reason to pursue the known options within his or her grasp. If this leads to the anticipated success, it is likely the product not of innovation but of ordinary skill and common sense.
[Claim 10] Jaggers appears to be silent on wherein the intrapersonal multiple intelligence is self-awareness, self-reflection, goal setting, metacognition, mindfulness, autonomy, resilience or a combination thereof. However, it is common knowledge, among other things, that intrapersonal learners “are self-motivated and very self-aware to the point of being overly critical of themselves”, “like to study alone because they need the time to process their thoughts internally”, “Avoid class discussions and group projects”, and “Have very high self-management skills”7. Hence, it would have been prima facie obvious to one of ordinary skill in the art, before the effective filing date of the invention, to have modified Jaggers as claimed because a person of ordinary skill has good reason to pursue the known options within his or her grasp. If this leads to the anticipated success, it is likely the product not of innovation but of ordinary skill and common sense.
In reference to the above rejections, it is appropriate to rely on prior art not specifically relied upon in the rejection as evidence that certain subject matter is well known in the art. See Randall Mfg. v. Rea, 733 F.3d 1355, 1362–63 (Fed. Cir. 2013) (prior art of record not specifically relied upon in the rejection considered as evidence of the state of the art, common knowledge, and common sense of one of ordinary skill in the art). Further, an artisan must be presumed to know something about the art apart from what the references disclose. See In re Jacoby, 309 F.2d 513, 516 (CCPA 1962).
Claim 11 is rejected under 35 U.S.C. 103 as obvious over Jaggers, as applied to claim 1 above, in view of Shriberg et al. (US 20190385711 A1) (Shriberg).
Re claim 11:
[Claim 11] Jaggers appears to be silent on but Shriberg teaches or at least suggests wherein the computer program normalizes and cleans said photos and videos to ensure data quality (at least ¶ 310: The filtered social, demographic, and clinical data, speech and video data, and label data are all provided to a preprocessor 2031 for cleaning and normalization of the filtered data sources). It would have been prima facie obvious to one of ordinary skill in the art, before the effective filing date of the invention, to have utilized the cleaning and normalization features of Shriberg to modify Jaggers as claimed because this would amount to no more than applying known techniques to a known device (method, or product) ready for improvement to yield predictable results. See KSR Int’l Co. v. Teleflex Inc., 550 U.S. 398, 416 (2007) (“The combination of familiar elements according to known methods is likely to be obvious when it does no more than yield predictable results.”).
Claim 12 is rejected under 35 U.S.C. 103 as obvious over Jaggers, as applied to claim 1 above, in view of Sait et al. (US 20250148931 A1) (Sait).
Re claim 12:
[Claim 12] Jaggers appears to be silent on but Sait teaches or at least suggests wherein the computer program comprises a step of creating a feedback loop to continuously improve the machine learning models based on new data and prediction outcomes (at least ¶ 37: FIG. 2b depicts the pipeline as a feedback loop that constantly updates the AI composite model powering the system and updates the personalized education provided to the user; ¶ 41: In continuous training, the user feedback may be used as input data for the automated machine learning pipeline in that the data may be processed, subject to feature engineering, and be used for model tuning. The deployment of this feedback loop can continuously improve the AI model in real-time in order to provided the most suitable and tailored education for the user to ensure satisfaction). It would have been prima facie obvious to one of ordinary skill in the art, before the effective filing date of the invention, to have utilized Sait’s teaching of a feedback loop that can continuously improve an AI model, to modify Jaggers as claimed because this would amount to no more than applying known techniques to a known device (method, or product) ready for improvement to yield predictable results. See KSR Int’l Co. v. Teleflex Inc., 550 U.S. 398, 416 (2007) (“The combination of familiar elements according to known methods is likely to be obvious when it does no more than yield predictable results.”).
Claims 17 and 18 rejected under 35 U.S.C. 103 as obvious over Jaggers, as applied to claim 13 above, in view of McClernon et al. (US 20200117901 A1) (McClernon).
Re claims 17 and 18:
[Claim 17] Jaggers appears to be silent on but McClernon teaches or at least suggests wherein the machine learning model training module is adapted to implement ensemble learning techniques for improved prediction accuracy (at least ¶ 18: FIG. 5 is an illustration of the classification model, which extracts image features using the Inception v4 convolutional neural network; ¶ 89: A type of deep learning model called the convolutional neural network (CNN) can be applied to identify objects and settings present in the image or make other image-related predictions …). It would have been prima facie obvious to one of ordinary skill in the art, before the effective filing date of the invention, to have utilized convolutional neural network (CNN) as taught by McClernon to modify Jaggers as claimed because this would amount to no more than applying a known technique to a known device (method, or product) ready for improvement to yield predictable results. See KSR Int’l Co. v. Teleflex Inc., 550 U.S. 398, 416 (2007) (“The combination of familiar elements according to known methods is likely to be obvious when it does no more than yield predictable results.”).
[Claim 18] Jaggers in view of McClernon teaches or at least suggests a recommendation module that provides actionable insights based on the generated predictions (at least Jaggers: ¶ 113: predict and schedule subsequent service calls for the customer, to collect and analyze reliability information for the machine, system or device and to suggest replacement thereof when reliability information indicates that the machine, device, or system will soon reach its end of life or repairs will become too expensive).
Conclusion
The prior art made of record and not relied upon is listed in the attached PTO
Form 892 and is considered pertinent to applicant's disclosure.
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/EDDY SAINT-VIL/Primary Examiner, Art Unit 3715
1 https://engage-education.com/blog/learning-styles-kinaesthetic-learner-characteristics/
2 https://blog.bjupress.com/blog/2022/07/12/logical-learner-characteristics-strategies-and-activities/
3 https://www.spanish.academy/blog/8-language-learning-styles-which-type-is-yours/
4 https://busyteacher.org/15551-how-to-teach-musical-learners-9-ways.html
5 https://www.thoughtco.com/naturalist-intelligence-8098
6 https://uteach.io/articles/interpersonal-learning
7 https://rockwoodprep.com/intrapersonal-leaners/
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