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 .
Response to Arguments
Applicant's arguments filed 28 November 2025 have been fully considered but they are persuasive only in part.
First, applicant traverses the specification objection saying that e.g., “the size and number of the individual obstacles” and “their base area or diameter” provides proper antecedent basis in the specification for the “new claim terminology” (see MPEP 608.01(o)) of “generating . . . hypotheses concerning . . . the at least one computed geometric feature of the individual detected obstacles”. Since the new claim terminology “geometric feature” apparently is different than (and has a different claim scope relative to) the size or number of obstacles or their base area or diameter, this argument cannot be persuasive.
Second, applicant’s claim amendments obviate/overcome the claim objection.
Third, regarding the enablement and written description requirement rejections under 35 U.S.C. 112(a), applicant points to the claim amendments and concludes that they comprise “subject matter clearly derived from the specification, as to enable one skilled in the art to which it pertains, or with which it is most nearly connected, to make and/or use the claimed subject matter (See the Application, III [0007] - [0008], III [0034] - [0037], and I [0057]).” The examiner does not understand the basis for applicant’s conclusion, and therefore repeats the rejections with reasonings in support of his rejections. In this respect, the claims apparently still cover and encompass “generating” a near infinite number of “hypotheses” and “testing” the near infinite number of hypotheses. Applicant has neither enabled nor described, in sufficient detail, such a broad range of hypotheses generation and testing. See MPEP 2161.01, I. and LizardTech Inc. v. Earth Resource Mapping Inc., 424 F.3d 1336, 1345 (Fed. Cir. 2005) cited therein ("Whether the flaw in the specification is regarded as a failure to demonstrate that the applicant possessed the full scope of the invention recited in [the claim] or a failure to enable the full breadth of that claim, the specification provides inadequate support for the claim under [§ 112(a)]").
Fourth, regarding the rejections under 35 U.S.C. 112(b), applicant asserts, “Claims 1, and 13 - 17 have been amended to overcome each 35 U.S.C. § 112(b) rejection and are now believed to be in condition for allowance. In this manner, appropriate scope and basis have been provided to the above identified claim elements within the Claims 1 and 13 - 17, such that the recitations identified within the Office Action have been corrected within Claims 1 and 13 - 17.” The examiner believes that numerous issues in the claims, regarding terms with unclear metes and bounds, etc., are still present in the claims, as detailed below.
Fifth, regarding the rejection under 35 U.S.C. 101, applicant apparently argues that the claims solve a problem related to “comparatively small obstacles” “not being taken into account” by the robot. However, not taking (or taking) the small obstacles into account, without any claimed concomitant robotic control or operation, is apparently only an abstract idea, and thus not an improvement to the functioning of a computer, or to any other technology or technical field - see MPEP 2106.05(a). Moreover, the claims do not even require any “small obstacles” be present in the area of robot operation. Additionally, in the specification, applicant himself characterizes the sectoring of a robot area map, even with the aid of sensors, as being “abstract methods” (published paragraph [0004], two occurrences). Accordingly, applicant’s arguments are not convincing in this respect.
Applicant next argues that the “sensors on the autonomous mobile robot”1 (and the correlated data collected from sensors) are specific and non-generic to constitute “additional elements” in the 101 analysis. However, applicant apparently indicates at published paragraph [0004] of the specification that a camera and/or a distance sensor of the robot, as well as other sensors to detect the borders of floor coverings, are apparently well-understood, routine, conventional in the field of autonomous mobile (cleaning) robots. Moreover, the correlated “data” (from the sensors), being itself abstract and indefinite, is also not sufficient to constitute an additional element that would indicate integration into a practical application, representing only insignificant extra-solution activity. Accordingly, applicant’s arguments are not persuasive in this respect.
Applicant further argues:
The practical application in the claimed subject matter is the elimination of manual user-inputted identification of obstacles and/or user-inputted sectoring, which dramatically streamlines the efficiency of robot operation within a predetermined area by leveraging correlated data obtained from immutable hardware.
However, this improvement is apparently not described in the specification, which instead indicates, “[I]n response to a user input concerning the specifiable parameters, the sectoring of the area of robot operation into zones, the position of doors and/or the designation of the functions of the detected zones is maintained and the sectoring of the area of robot operation is altered in dependence on the user input” (published paragraph [0009]) and “In the simplest case, the robot can test an automatedly generated hypothesis by “asking” the user, i.e. by requesting the user's feedback. The user can then either confirm or reject the hypothesis” (published paragraph [0034]). Moreover, since the claims do not require the elimination of manual user-inputted identification of obstacles and/or user-inputted sectoring, this argued improvement is apparently not reflected in the claims. Accordingly, applicant’s arguments are not fully convincing in this respect.
However, in view of the amendments to claims 15, 16, and 17, the examiner withdraws the rejections under 35 U.S.C. 101 of these claims, understanding limitations in these claims (e.g., through dependency) as apparently being indicative of integration into a practical application, e.g., by operating, using the processor, the autonomous mobile robot according to the sectored map e.g., in order to (through such operation) effect (through the robot operation) a transformation or reduction of a particular article to a different state or thing - see MPEP 2106.05(c).
Lastly, due to the confusion and uncertainty as to the proper interpretation of the limitations of the claims with respect to the issues under 35 U.S.C. 112 noted herein below, the examiner chooses to withdraw the rejection under 35 U.S.C. 103, since a rejection under 35 U.S.C. 103 should not be based on considerable speculation about the meaning of terms employed in a claim or assumptions that must be made as to the scope of the claims. See MPEP 2173.06, II.
Accordingly, applicant’s arguments are only persuasive in part.
Specification
The specification is objected to as failing to provide proper antecedent basis for the claimed subject matter. See 37 CFR 1.75(d)(1) and MPEP § 608.01(o)2. Correction of the following is required: antecedent basis should be provided in the specification for the following new claim terminologies:
● “generating . . . hypotheses concerning . . . the at least one computed geometric feature of individual detected obstacles”,
● “based on a determination that the tested generated hypotheses exceed a predetermined point threshold, maintaining the boundary lines of the individual zones and/or the at least one computed geometric feature of the individual detected obstacle”, and
● “based on a determination that the tested generated hypotheses do not exceed a predetermined point threshold, adjusting, in real-time, at least one boundary line of the individual zones and/or the at least one computed geometric feature”,
without adding new matter.
In this respect, for example, published paragraph [0044] teaches that hypotheses regarding difficult-to-pass zones may be “generated on the basis of desired features of the zone to be created and of a complementary zone”, but this does not describe “generating . . . hypotheses concerning . . . the at least one computed geometric feature of individual detected obstacles”, as is now claimed. The examiner also has found no terminology in the specification relating to the “maintaining” or “adjusting, in real time” of the claim elements, as now claimed.
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1, 11, and 13 to 17 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the enablement requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to enable one skilled in the art to which it pertains, or with which it is most nearly connected, to make and/or use the invention.
Regarding independent claim 1, applicant has not enabled the full breadth3 of generating any or all hypotheses concerning any or all computed geometric features (e.g., for limited examples only, possibly concerning radius/radii of curvature, passages or through-holes, angle range of movable joints, symmetry plane(s), height discontinuities or overhangs, etc.) of any or all individual detected obstacles.
Here, the examiner believes that the number of possible generated “hypotheses” concerning any or all computed geometric “features”4 of any or all individually detected obstacles (e.g., whether the obstacle is round/circular or has a particular radius of curvature, whether the obstacle has a passage or through-hole, whether the obstacle is discontinuous in the height direction or has an overhang, whether an obstacle has movable joints, whether the obstacle is symmetric relative to one or more reflection planes, etc., etc.) that the claim would cover and encompass is near infinite, while applicant has only described in the specification a small number of (partial) examples of hypotheses in the specification, which are not representative of the entire genus of any or all possible generated hypotheses concerning computed geometric features as covered by the claim. (Regarding representative species for a genus, see e.g., MPEP 2163, II., A., 3., (a), ii), and (b).)
For example, the specification teaches this at published paragraphs [0034], [0037], and [0040]:
In order to solve the above mentioned problems and to allow for an automated sectoring of the area of robot operation into various zones (e.g. rooms), the robot generates, based on the sensor data, “hypotheses” about its environment that can be tested using various methods. If a hypothesis can be proved false, it is rejected. If two boundary lines (e.g. the lines A-A′ and O-O′ in FIG. 1) are more or less parallel and are at a distance to each other that corresponds to the common clear width of a door frame (for this there are standardized values), then the robot will generate the hypothesis “door frame” and conclude from this that the two lines separate two different rooms. In the simplest case, the robot can test an automatedly generated hypothesis by “asking” the user, i.e. by requesting the user's feedback. The user can then either confirm or reject the hypothesis. A hypothesis can also be tested automatedly, however, in which case the plausibility of the conclusions drawn from the hypothesis is tested. If the rooms detected by the robot (e.g. by means of detection of the door thresholds) include a central room that, e.g., is smaller than one square meter, then the hypothesis that ultimately led to this small central room is probably false. A further automated test may consist in testing whether or not conclusions drawn from two hypotheses contradict each other. If, for example, six hypotheses indicating a door can be generated and the robot can only detect a door threshold (a small step) in the case of five of the assumed doors, then this may be an indication that the hypothesis that indicates a door without a threshold is false.
To test and evaluate hypotheses, a degree of plausibility may be assigned to them. In one simple embodiment, a hypothesis is credited with a previously specified number of points for every confirming sensor measurement. When, in this manner, a certain hypothesis achieves a minimum number of points, it is regarded as plausible. A negative total number of points could result in the hypothesis being rejected. In a further developed embodiment, a probability of being correct is assigned to a certain hypothesis. This requires a probability model that takes into account the correlation between various sensor measurements but also allows complex probability statements to be generated with the aid of stochastic calculation models, thus resulting in a more reliable prediction of the user's expectations. For example, in certain regions (i.e. countries) in which the robot is operated, the width of doors may be standardized. If the robot measures such a standardized width, then this most probably relates to a door. Deviations from the standard widths reduce the probability that they relate to a door. For this purpose, for example, a probability model based on a standard distribution may be used. A further possibility for the generation and evaluation of hypotheses is the use of “machine learning” to generate suitable models and measurement functions (see, e.g. Trevor Hastie, Robert Tibshirani, Jerome Friedman: “The Elements of Statistical Learning”, 2nd edition, Springer Publishing House, 2008). For this purpose, for example, map data is gathered by one or more robots in various living environments. The data can then be supplemented with floor plans or further data input by the user (e.g. regarding the run of doors or doorways or regarding a desired sectoring) and can then be evaluated by a learning algorithm.
Based on the assumption that rooms are generally rectangular, the robot can supplement the outer boundary lines of the map of boundary lines (see FIG. 1) to form a rectilinear polygon. The result of this is shown in FIG. 2. It is also possible to place a rectangle through the outer boundary lines of the apartment (see FIG. 2, rectangle that encompasses the apartment W and the inaccessible area X) and to remove from them inaccessible areas (see FIG. 2, area X). Based on detected doors (see FIG. 1, door threshold between the points O and A, as well as between P′ and P″) and inner walls (see FIG. 1, antiparallel boundary lines in the distance d m), the apartment can be automatedly sectored into three rooms 100, 200 and 300 (see FIG. 3). When doing so, areas determined to be walls are extended to a door or to the outer boundary of the apartment. Inaccessible areas within the rooms can be interpreted by the robot to be pieces of furniture or other obstacles and can be correspondingly designated on the map (see FIG. 4). For example, the piece of furniture 101 may even, based on its dimensions (distances separating the boundary lines), be identified as a bed (beds have standardized sizes) and consequently, room 100 can be identified as a bedroom. The area 102 is identified as a chest of drawers. However, it may also be a shaft or a chimney.
The specification teaches this at published paragraph [0036]:
However, additional objects (obstacles) such as, for example, wardrobes, shelves, flower pots, etc. may also be standing against a wall and may be able to be identified with the aid of hypotheses. One hypothesis may rest upon another hypothesis. For example, a door is a discontinuation of a wall; so when reliable hypotheses about the run of walls in the area of robot operation can be generated, these may be used to identify doors and to thus simplify the automated sectoring of the area of robot operation.
Published paragraph [0071] teaches this:
If the user furnishes a zone recognized as a room with the designation “bedroom”, various criteria—in particular the probability models used to generate hypotheses—can be adapted to those of a typical bedroom during the further automated sectoring of the bedroom. In this manner, an object found in the bedroom having the dimensions of one by two meters may with relative reliability be interpreted to be a bed. In a room designated as a “kitchen”, an object of similar dimensions might possibly be determined to be a kitchen island.
However, these and other specification passages do not apparently describe the generation of any or all hypotheses related to any or all computed geometric features of “wardrobes, shelves, flower pots, etc.” and/or obstacles other than a door frame, and they do not enable the full breadth of generating any or all of a near infinite number of hypotheses concerning a near infinite number of “computed geometric features” (e.g., shape, symmetry, dimensional characteristics, etc.) of a near infinite number of obstacles that may be present in a room (e.g., a crowbar, a diaper, a basketball, a saw or knife, a black cat or other pet, a Christmas tree, a Covid mask, scattered pieces of children’s toys, etc.) Accordingly, the examiner believes applicant has not enabled those skilled in the art to make and use the full breadth of the claimed invention.
For example, if the obstacle was a lounge chair and the computed geometric feature was a caster or wheel at the bottom of the chair, then how was a hypothesis concerning the geometric feature generated as to whether the caster or wheel would allow the autonomous mobile robot to autonomously move/push the chair out of its way, from the teachings of the specification? Accordingly, the examiner believes applicant has not enabled those skilled in the art to make and use the full breadth of the claimed invention.
Here, the examiner merely notes that the specification apparently refers to no generated hypotheses concerning computed geometric features of the individual detected obstacles, but rather refers to “hypotheses concerning . . . the function of individual detected obstacles” (published paragraph [0008], see also original claims 1, 5, and 12). A “function” of an obstacle is apparently NOT a “computed geometric feature” of the obstacle.
Because the breadth of the claims is so large as to cover the generation of any or all hypotheses concerning any or all computed geometric feature(s) of individual detected obstacles, even hypotheses and features applicant has not envisioned, because the nature of the invention (generating hypotheses concerning the feature(s)) is by nature highly complex, because the state of the prior art is not well-developed, because the level of one of ordinary skill in the art cannot make up for the lack of teaching in the disclosure to enable all generation of hypotheses for all computed geometric features of all obstacles, since these would be nearly infinite, because the level of predictability in the art is implicitly low since any hypotheses may or may not be “considered to be plausible”, because the amount of direction and/or working examples provided by the inventor is minimal (e.g., hypotheses for determining a bed in a bedroom and an island in a kitchen or a door frame are provided, while millions/billions of generated hypotheses, limited only by imagination, may be covered by the claim of which these three examples are not representative, e.g., whether an obstacle is a pet, whether an obstacle is round or symmetric or has overhangs, etc.) or non-existent for most hypotheses, the examiner believes that undue experimentation on the part of the public would be required to implement the full breadth5 of the claimed invention. See MPEP 2164.01(a).
Regarding independent claim 1, applicant has not enabled the full breadth of testing any or all generated hypotheses using any or all probability models and correlated data collected from the robot obstacle detection sensors.
Here, the examiner believes that there are a near infinite number of “hypotheses” that the claim covers and encompasses (i.e., to exclude others from making or using), and a similarly large number of (correlated sensor data based) tests that might be conducted for such hypotheses, while applicant has only described in the specification a small number of (partial) examples of tests in the specification, which are not representative of the entire genus of any or all possible tests for hypotheses covered by the claim.
For example, the specification teaches this at published paragraph [0034], [0035], and [0037]:
[0034] In order to solve the above mentioned problems and to allow for an automated sectoring of the area of robot operation into various zones (e.g. rooms), the robot generates, based on the sensor data, “hypotheses” about its environment that can be tested using various methods. If a hypothesis can be proved false, it is rejected. If two boundary lines (e.g. the lines A-A′ and O-O′ in FIG. 1) are more or less parallel and are at a distance to each other that corresponds to the common clear width of a door frame (for this there are standardized values), then the robot will generate the hypothesis “door frame” and conclude from this that the two lines separate two different rooms. In the simplest case, the robot can test an automatedly generated hypothesis by “asking” the user, i.e. by requesting the user's feedback. The user can then either confirm or reject the hypothesis. A hypothesis can also be tested automatedly, however, in which case the plausibility of the conclusions drawn from the hypothesis is tested. If the rooms detected by the robot (e.g. by means of detection of the door thresholds) include a central room that, e.g., is smaller than one square meter, then the hypothesis that ultimately led to this small central room is probably false. A further automated test may consist in testing whether or not conclusions drawn from two hypotheses contradict each other. If, for example, six hypotheses indicating a door can be generated and the robot can only detect a door threshold (a small step) in the case of five of the assumed doors, then this may be an indication that the hypothesis that indicates a door without a threshold is false.
[0035] Various sensor measurements are combined when a hypothesis is tested by the robot. In the case of a doorway, for example, the tested measurements include the passage width, the passage depth (given by the thickness of the wall), the existence of a wall to the right and to the left of the doorway or of an open door extending into the room. All this information can be detected, for example, by the robot with the aid of a distance sensor. By means of an acceleration sensor or of a position sensor (e.g. a gyroscopic sensor), the possible existence of a door threshold can be detected when the robot passes over it. With the use of image processing and by measuring the height of the ceiling, additional information can be gathered.
[0037] To test and evaluate hypotheses, a degree of plausibility may be assigned to them. In one simple embodiment, a hypothesis is credited with a previously specified number of points for every confirming sensor measurement. When, in this manner, a certain hypothesis achieves a minimum number of points, it is regarded as plausible. A negative total number of points could result in the hypothesis being rejected. In a further developed embodiment, a probability of being correct is assigned to a certain hypothesis. This requires a probability model that takes into account the correlation between various sensor measurements but also allows complex probability statements to be generated with the aid of stochastic calculation models, thus resulting in a more reliable prediction of the user's expectations. For example, in certain regions (i.e. countries) in which the robot is operated, the width of doors may be standardized. If the robot measures such a standardized width, then this most probably relates to a door. Deviations from the standard widths reduce the probability that they relate to a door. For this purpose, for example, a probability model based on a standard distribution may be used. A further possibility for the generation and evaluation of hypotheses is the use of “machine learning” to generate suitable models and measurement functions (see, e.g. Trevor Hastie, Robert Tibshirani, Jerome Friedman: “The Elements of Statistical Learning”, 2nd edition, Springer Publishing House, 2008). For this purpose, for example, map data is gathered by one or more robots in various living environments. The data can then be supplemented with floor plans or further data input by the user (e.g. regarding the run of doors or doorways or regarding a desired sectoring) and can then be evaluated by a learning algorithm.
However, these and other specification passages do not enable the full breadth of all testing of any or all hypotheses concerning any or all computed geometric features of any or all obstacles based on any or all probability models and correlated sensor data that may be used to detect obstacles. For example, if the generated hypothesis is that the obstacle is a pet that has legs as geometrical features and moves, then how was that hypothesis tested (and how would it be tested by one having ordinary skill in the art) with correlated data collected by e.g., a distance sensor or a camera arranged on the robot, from the teachings of the specification? Accordingly, the examiner believes applicant has not enabled those skilled in the art to make and use the full breadth of the claimed invention.
Because the breadth of the claims is so large as to cover any or all testing of any or all generated hypotheses concerning a computed geometric feature of individual detected obstacles using the data collected from the sensors, because the nature of the invention (testing generated hypotheses concerning the feature) is by nature highly complex, because the state of the prior art is not well-developed, because the level of one of ordinary skill in the art cannot make up for the lack of teaching in the disclosure to enable all testing of generated hypotheses for all features of all obstacles, since these would be nearly infinite, because the level of predictability in the art is implicitly low since any tested hypotheses may or may not be “considered to be plausible”, because the amount of direction and/or working examples provided by the inventor is minimal (e.g., no explicit testing hypotheses for determining a bed in a bedroom and an island in a kitchen is apparently provided, while millions/billions of tested hypotheses, limited only by imagination, may be covered by the claim of which these two hypotheses examples are not representative, e.g., whether an obstacle is a pet, whether an obstacle is symmetric or has a particular radius of curvature, etc.) and non-existent for most hypotheses, the examiner believes that undue experimentation on the part of the public would be required to implement the full breadth of the claimed invention. See MPEP 2164.01(a).
Claims 1, 11, and 13 to 17 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention.
Regarding independent claim 1, applicant has apparently not described, in sufficient detail, by what algorithm(s)6, or by what steps or procedure, he computed any or all geometric features of the individual detected obstacles as may be covered by the claim. No computation of geometric features such as radius/radii of curvature, passages or through-holes, angle range of movable joints, symmetry plane(s), height discontinuities or overhangs, etc., etc. is apparently described, in sufficient detail, in the specification, commensurate with the claim scope. In fact, no such “geometric features” other than possibly a door width at published paragraph [0005] are apparently described, in sufficient detail. Accordingly the examiner believes that applicant has not evidenced, to those skilled in the art, possession of the full scope7 of the claimed invention, but has only (now) described a desired result.
For example, applicant has apparently not described, in sufficient detail, how he computed any radius of curvature of the obstacle, or computed whether the obstacle included passages or through holes or plane(s) of symmetry or height discontinuities or overhangs (or a perhaps near infinite number of other features that might relate to geometry) as geometric features of the obstacles, e.g., using sensors such as a camera, etc. In this respect, the only geometric feature described as such in the specification is the width of the door frame in published paragraph [0005]. Accordingly the examiner believes that applicant has not evidenced, to those skilled in the art, possession of the full scope of the claimed invention, but has only (now) described a desired result.
Regarding independent claim 1, applicant has apparently not described, in sufficient detail, by what algorithm(s), or by what steps or procedure, he generated any or all hypotheses concerning the at least one computed geometric feature of individual detected obstacles based on the detected obstacles. Accordingly, the examiner believes that applicant has not evidenced, to those skilled in the art, possession of the full scope of the now claimed invention.
For example, the only feature apparently explicitly described as a “geometric feature” in applicant’s specification is apparently the width of the door at paragraph [0005]. But the claimed “geometric feature” of the individual detected obstacles is not limited to a door width (published specification paragraph [0005]), but would encompass a perhaps unlimited number of other “geometric feature[s]” of other “obstacles”, with the specification providing NO LIMITS (and only a very few examples) on what the “geometric feature” of the “obstacles” might possibly be or be referring to (e.g., possibly a shape, height, curvature, hole, passageway, overhang, symmetry, flexibility, of e.g., a crowbar, a diaper, a basketball, a saw or knife, a black cat or other pet, a Christmas tree, a Covid mask, scattered pieces of children’s toys, etc. as obstacles, among an unlimited number of geometric features and obstacles for which the few examples in the specification do not/cannot evidence possession of the full scope of the invention).
The examiner believes that no algorithm(s) or steps/procedure for generating any or all hypotheses, of the near unlimited number of hypotheses that the claim covers and encompasses, regarding a geometric feature of individual detected obstacles, of the near unlimited number of geometric features that the claim encompasses and covers and near unlimited number of obstacles that the claim encompasses and covers, is/are apparently described, in sufficient detail, in the specification, commensurate with the claim scope. Only a very few examples of or allusions to what a generated hypothesis for and obstacle might be are provided in the specification (see e.g., published paragraph [0071] for two possible hypotheses), and these few examples are not representative of the entire genus of any or all possible hypotheses covered by the claim. See MPEP 2163, II., A., 3., (a), ii), and (b). Accordingly, the examiner believes that applicant has not evidenced, to those skilled in the art, possession of the full scope of the now claimed invention, but only now describes a “desired result”.
Here, the examiner merely notes that the specification apparently refers to no generated hypotheses concerning computed geometric features of the individual detected obstacles, but rather refers to “hypotheses concerning . . . the function of individual detected obstacles” (published paragraph [0008], see also original claims 1, 5, and 12). A “function” of an obstacle is apparently NOT a “computed geometric feature” of the obstacle.
Regarding independent claim 1, applicant has apparently not described, in sufficient detail, by what algorithm(s), or by what steps or procedure, he tested any or all of the generated hypotheses using any or all probability models and correlated data collected from the sensors. No algorithm(s) that would test any or all generated hypotheses as claimed, commensurate with the full scope of the claim, is/are apparently described, in sufficient detail, and examples representative of the entire genus of testing the hypotheses are not apparently described in the specification, in sufficient detail. (See MPEP 2163, II., A., 3., (a), ii), and (b).) For example, if the generated hypothesis is that the obstacle has a particular radius of curvature or is characterized by a movable joint or an overhang, then where is it described by what algorithm(s) or steps/procedure that hypothesis was tested using a probability model and correlated data collected by e.g., a distance sensor or a camera arranged on the robot, from the teachings of the specification? Accordingly, the examiner believes that applicant has not evidenced, to those skilled in the art, possession of the full scope of the now claimed invention, but only now describes a “desired result”.
Regarding independent claim 1, applicant has apparently not described, in sufficient detail, by what algorithm(s), or by what steps or procedure, he sectored the map area of robot operation into zones by maintaining or adjusting in real-time the boundary line(s) and/or at least one computed geometric feature based on the determination that the hypotheses exceed or do not exceed “a predetermined point threshold” (which is indefinite in the claim). No such algorithm(s) that would sector the map area into zones in the manner claimed based on e.g., any or all tested generated hypotheses regarding geometric features of individual detected obstacles, etc., commensurate with the full scope of the claim, is/are apparently described, in sufficient detail, and examples representative of the entire genus of sectoring the map area based on the tested generated hypotheses are not apparently described in the specification, in sufficient detail. (See MPEP 2163, II., A., 3., (a), ii), and (b).). For example, no “maintaining” or “adjusting in real-time” of the boundary lines or a boundary line or of the at least one computed geometric feature based on the tested generated hypothesis exceeding or not exceeding the/any predetermined point threshold8 is apparently described, in sufficient detail, in the specification. Accordingly, the examiner believes that applicant has not evidenced, to those skilled in the art, possession of the full scope9 of the now claimed invention, but only now describes a “desired result”.
Claims 1, 11, and 13 to 17 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
In claim 1, lines 6ff, “at least one geometric feature of the individual detected obstacles” is indefinite grammatically (e.g., is a single/common “geometric feature” possessed by (“of”) the plural “individual detected obstacles” as the plain meaning would indicate? if so, how?), in the claim context (e.g., particularly how would the size and position of obstacles be determined “by computing” the geometric feature(s)?), and from the teachings of the specification that provides no metes and bounds10 for what would or would not constitute any or all “geometric feature[s]” as are covered by the claim (e.g., might the claimed “geometric feature” be, or even be the same as, the previously claimed “size and position of the obstacles”, e.g., as an unclear double inclusion/recitation of the same claim element by two different names? See MPEP 2173.05(o).)
In claim 1, lines 7ff, “generating, using a processor, based on the detected obstacles, hypotheses concerning boundary lines of zones, the at least one computed geometric feature of the individual detected obstacles, or hypotheses concerning boundary lines of the zones and the at least one computed geometric feature of the individual detected obstacles” is indefinite and not reasonably certain from the teachings of the specification (that does not clarify the metes and bounds of any “computed geometric feature of the individual detected obstacles”, nor even define with reasonable certainty what the newly claimed “geometric feature” might possibly be) and in the claim context (e.g., that does not provide any context or limits on what “zones” might possibly be referring to, such as zones of an actual room, house, or other real-world area, or possibly zones of the digital or a tangible map, etc.), which does not provide reasonably certain metes and bounds of what the “hypotheses” “concerning” e.g., the geometric features of obstacles might possibly be.
For example, the Free Dictionary defined hypothesis as:
hy·poth·e·sis (hī-pŏth′ĭ-sĭs) n. pl. hy·poth·e·ses (-sēz′)
1. A tentative explanation for an observation, phenomenon, or scientific problem that can be tested by further investigation.
2. Something taken to be true for the purpose of argument or investigation; an assumption.
3. The antecedent of a conditional statement.
. . .
[From: American Heritage® Dictionary of the English Language, Fifth Edition. Copyright © 2016 by Houghton Mifflin Harcourt Publishing Company. Published by Houghton Mifflin Harcourt Publishing Company. All rights reserved. Retrieved 23 May 2025.]
However, the metes and bounds of any or all generated “tentative explanation[s] for an observation, phenomenon, or scientific problem that can be tested by further investigation”, a.k.a. hypotheses, are unclear in the claim context, and from the teachings of the specification, especially when the zones and geometric features are indefinite in the claims, and the claims are not limited to any specific observations, phenomena, or scientific problems, nor is it clear when hypotheses might “concern[]” e.g., computed geometric features.
In claim 1, line 15, “correlated data collected from the sensors” is indefinite and unclear from the teachings of the specification (e.g., correlated11 in what way or how, particularly?).
In claim 1, line 17, “zones” is indefinite and unclear in the claim context, because “”zones” have already been recited in line 10 (and in line 12), and so it is unclear whether the “zones” of line 17 are intended to be the same as, different from, permissively the same as, permissively different from, necessarily the same as, necessarily different from, etc. the previously recited zones in line 10.
In claim 1, lines 18 to 24 are unclear in their entireties, e.g., for lacking apparent clarifying/antecedent basis in the specification and for lacking sufficient support in the specification, even when considering clearly equivalent terms12, for interpreting the claim language “in light of the specification” (MPEP 2111). See also 37 CFR 1.75(d)(1).
In this respect, in claim 1, “exceed a predetermined point threshold” (in line 19 and in lines 22ff) is vague and indefinite in the claim context (e.g., which predetermined “point threshold” defined particularly how in the claim so as to have reasonably certain metes and bounds?), with the examiner being unwilling to import claim limitations from specification’s published paragraphs [0037], [0039], [0040], etc. regarding the metes and bounds of the claimed “a predetermined point threshold”. See MPEP 2111.01, II.
Similarly, in claim 1, “maintaining the boundary lines . . . and/or the at least one computed geometric feature” in lines 19ff is indefinite (e.g., “maintaining” in what particularly and in what way particularly, for how long, etc.?) with the specification apparently not clarifying how or that either boundary lines and/or geometric feature[s] would be “maintain[ed]” based on the claimed condition.
Similarly, in claim 1, “adjusting in real-time, at least one boundary line . . . and/or the at least one computed geometric feature” in lines 23ff is indefinite (e.g., “adjusting” in what particularly, what does “real-time” mean in this context since the specification apparently describes no such real-time adjusting, etc.) with the specification apparently not clarifying how or that either boundary lines and/or geometric feature[s] would be “adjust[ed] in real-time” based on the claimed condition.
In claim 14, lines 2ff, “the step of further comprising the step of, assigning, using a calendar function of the processor, an identity to each of the individual zones” is fully indefinite, being indefinitely grammatically (e.g., why does the step further comprise the step?) and in the claim context, and with the “calendar function” being indefinite both in the claim context (e.g., what characteristics must a function have to be considered a “calendar function”?) and from the teachings of the specification (which does not clarify the calendar function with metes and bounds or indicate particularly how such a “calendar function” might assign an “identity to each of the individual zones”.
In claim 15, line 3, “according to the sectored map” is unclear because it is unclear which one (e.g., the first, the second, either, or both?) of the two claimed “sectoring” acts/steps (e.g., the first being in claim 1 and the second being in claim 14) need be performed on the map of the area of robot operation in order for the map to become/be considered “the sectored map” as recited/required in claim 15, and for the autonomous mobile robot to thus be operated according to. (Here, the examiner merely notes that in claim 13, line 3, “the sectored map” is not considered to be indefinite, with the examiner understanding the phrase in the claim context to necessarily mean the map of the area of robot operation that has been sectored by the automatically sectoring act/step of claim 1.)
Claim(s) depending from claims expressly noted above are also rejected under 35 U.S.C. 112 by/for reason of their dependency from a noted claim that is rejected under 35 U.S.C. 112, for the reasons given.
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, 11, 13, and 14 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Step 1 and Step 2A, Prong I:
Claim(s) 1, 11, 13, and 14, while (each) reciting a statutory category of invention defined in 35 U.S.C. 101 (a useful process, machine, manufacture, or composition of matter), is/are directed to an abstract idea, which is a judicial exception, the recited abstract idea being that of determining a size and position of the obstacles on the map by computing at least one geometric feature of the individual detected obstacles, generating, using a processor (a brain) based on detected obstacles (e.g., detected by eyes, ears, etc.), hypotheses concerning boundary lines of zones, the at least one computed geometric feature of individual detected obstacles, or hypotheses concerning the boundary lines of the zones and the at least one computed geometric feature of individual detected obstacles; testing, using a probability model executed by the processor (e.g., the brain that understands and applies the concept of probability), the individual generated hypotheses using correlated data collected from the sensors (e.g., from the eyes, ears, etc.); and sectoring, using the processor, the map of the area (drawn on a piece of paper with a pencil or pen) of [e.g., robot, as a mere field of use limitation or recited at a high level of generality] operation into zones in the manner claimed by the maintaining or adjusting in real-time the boundary line(s) and/or geometric feature(s), e.g., by detecting, using sensors (e.g., eyes) arranged on the autonomous mobile robot (e.g., an autonomous human being, an automaton13), obstacles in the area of robot operation; determining, using the sensors arranged on the autonomous mobile robot, a size and position of the obstacles on the map by computing at least one geometric feature of the individual detected obstacles; generating, using a processor, based on the detected obstacles, hypotheses concerning boundary lines of zones, the at least one computed geometric feature of the individual detected obstacles, or hypotheses concerning boundary lines of the zones and the at least one computed geometric feature of the individual detected obstacles; testing, using a probability model executed by the processor, the individual generated hypotheses using correlated data collected from the sensors; automatically sectoring, using the processor, the map of the area of robot operation into zones by: based on a determination that the tested generated hypotheses exceed a predetermined point threshold, maintaining the boundary lines of the individual zones and/or the at least one computed geometric feature of the individual detected obstacle; and based on a determination that the tested generated hypotheses do not exceed a predetermined point threshold, adjusting, in real-time, at least one boundary line of the individual zones and/or the at least one computed geometric feature; further comprising: acquiring images of the area of robot operation by a camera (e.g., eyes) arranged on the robot; and recognizing in the images, via digital image processing, objects in the area of robot operation; wherein the step of automatically sectoring the map further comprises the step of, adjusting the boundary lines of the individual zones of the sectored map by a user using the human machine interface; wherein the step of automatically sectoring the map further comprises the step of further comprising the step of, assigning, using a calendar function of the processor, an identity to each of the individual zones.
This abstract idea falls within the grouping(s) of mathematical concepts, mental processes, and/or certain methods of organizing human activity, distilled from case law, because it, together with the acquiring, recognizing, maintaining, adjusting, and assigning (e.g., by an instruction or command from a user), could be practically performed in the human mind as a mental process, and furthermore, the computing of the at least one geometric feature and the determination of the exceeding/not exceeding of the point threshold, as well as the use/existence of any claimed geometric features themselves (e.g., a curvature, a width or size or distance, etc.) and the use of the probability model, comprise mathematical concepts14.
Step 2A, Prong II and Step 2B:
Additionally, applying a preponderance of the evidence standard, the abstract idea is not integrated (e.g., at Step 2A, Prong II) by the recitation of additional elements/limitations into a practical application (using the considerations set forth in MPEP §§ 2106.04(a)-(h)) because merely using a computer as a tool to perform an abstract idea or adding the words "apply it" is not integrating the idea into a practical application of the idea, and e.g., looking at the claim as a whole and considering any additional elements/limitations individually and in combination, no (additional) particular machine, transformation, improvement to the functioning of a computer or an existing technological process or technical field, or meaningful application of the idea, beyond generally linking the idea to a technological environment (e.g., "implementation via computers", Alice, and the environment of an autonomous mobile robot that is apparently never positively/clearly utilized in the rejected claims) or adding insignificant extra-solution activity (e.g., acquiring images from a camera), is recited in or encompassed by the claims.
Moreover, applying a preponderance of the evidence standard, the claim(s) does/do not include additional elements/limitations/steps (e.g., at Step 2B) that are, individually or in ordered combination, sufficient to amount to significantly more than the judicial exception because the elements/limitations/steps are recited at a high level of generality (e.g., the adjusting, the acquiring, the assigning, etc.) so as to not favor eligibility (MPEP § 2106.05(d)) and/or are used e.g., for data/information gathering only (e.g., correlated data collected from the sensors) or for other activities that were well-understood, routine, and conventional activity in the industry, for example as indicated in applicant's specification at published paragraphs [0002] to [0005] regarding the sectoring of robotic area maps with the aid of sensor data from the autonomous mobile robot, and moreover, the generically recited computer elements (e.g., a processor, sensors, a camera, a calendar function, etc.; see e.g., Alice Corp. Pty. Ltd. v. CLS Bank Int'l, 573 U.S. 208, 110 USPQ2d 1984 (2014); buySAFE, Inc. v. Google, Inc., 765 F.3d. 1350, 112 USPQ2d 1093 (Fed. Cir. 2014); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 115 USPQ2d 1090 (Fed. Cir. 2015); Intellectual Ventures I v. Symantec, 838 F.3d 1307, 1321, 120 USPQ2d 1353, 1362; Electric Power Group, LLC v. Alstom S.A., 830 F.3d 1350, 1354-1355, 119 USPQ2d 1739, 1742 (Fed. Cir. 2016); FairWarning IP, LLC v. Iatric Sys., Inc., 839 F.3d 1089, 1096 (Fed. Cir. 2016) (“[T]he use of generic computer elements like a microprocessor or user interface do not alone transform an otherwise abstract idea into patent-eligible subject matter.”); TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607 (transmitting images from a camera and “use of conventional or generic technology in a nascent but well-known environment, without any assertion that the invention reflects an inventive solution to any problem presented by combining a camera” with other equipment); Mobile Acuity, Ltd. v. Blippar Ltd., Case No. 22-2216 (Fed. Cir. Aug. 6, 2024); see also the 2019 PEG Advanced Module at pages 89, 145, etc.) do not add a meaningful limitation to the abstract idea because their use would be routine (and conventional) in any computer implementation of the idea.
Moreover, limiting or linking the use of the idea to a particular technological environment (e.g., an autonomous mobile robot, such as possibly e.g., “a person who works or behaves like a machine; automaton”, per the definition of “robot” in the footnote [1] above) is not enough to transform the abstract idea into a patent-eligible invention (Flook[15]) e.g., because the preemptive effect of the claims on the idea within the field of use would be broad.
Conclusion
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).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to David A Testardi whose telephone number is (571)270-3528. The examiner can normally be reached Monday, Tuesday, Thursday, 8:30am - 5:30pm E.T., and Friday, 8:30 am - 12:30 pm E.T.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Rachid Bendidi can be reached at (571) 272-4896. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/DAVID A TESTARDI/Primary Examiner, Art Unit 3664
1 robot (ˈrəʊbɒt) n
1. (General Engineering) any automated machine programmed to perform specific mechanical functions in the manner of a human
2. (General Engineering) (modifier) not controlled by man; automatic: a robot pilot.
3. a person who works or behaves like a machine; automaton
. . .
[From: Collins English Dictionary – Complete and Unabridged, 12th Edition 2014 © HarperCollins Publishers 1991, 1994, 1998, 2000, 2003, 2006, 2007, 2009, 2011, 2014. Retrieved 26 February 2026.]
2 Quoting the MPEP:
New claims, including claims first presented after the application filing date where no claims were submitted on filing, and amendments to the claims already in the application should be scrutinized not only for new matter but also for new terminology. While an applicant is not limited to the nomenclature used in the application as filed, he or she should make appropriate amendment of the specification whenever this nomenclature is departed from by amendment of the claims so as to have clear support or antecedent basis in the specification for the new terms appearing in the claims. This is necessary in order to insure certainty in construing the claims in the light of the specification. See 37 CFR 1.75, MPEP § 608.01(i) and § 1302.01 and § 2103. Note that examiners should ensure that the terms and phrases used in claims presented late in prosecution of the application (including claims amended via an examiner’s amendment) find clear support or antecedent basis in the description so that the meaning of the terms in the claims may be ascertainable by reference to the description, see 37 CFR 1.75(d)(1). If the examiner determines that the claims presented late in prosecution do not comply with 37 CFR 1.75(d)(1), applicant will be required to make appropriate amendment to the description to provide clear support or antecedent basis for the terms appearing in the claims provided no new matter is introduced.
3 See MPEP 2161.01, I. and LizardTech Inc. v. Earth Resource Mapping Inc., 424 F.3d 1336, 1345 (Fed. Cir. 2005) cited therein ("Whether the flaw in the specification is regarded as a failure to demonstrate that the applicant possessed the full scope of the invention recited in [the claim] or a failure to enable the full breadth of that claim, the specification provides inadequate support for the claim under [§ 112(a)]").
4 This broad/vague term “geometric feature[s]” as it may pertain to an object or obstacle is apparently only used in the specification context as referring to e.g., the “width of a door” (published paragraph [0005]).
5 See MPEP 2161.01, I. and LizardTech Inc. v. Earth Resource Mapping Inc., 424 F.3d 1336, 1345 (Fed. Cir. 2005) cited therein ("Whether the flaw in the specification is regarded as a failure to demonstrate that the applicant possessed the full scope of the invention recited in [the claim] or a failure to enable the full breadth of that claim, the specification provides inadequate support for the claim under [§ 112(a)]").
6 See the 2019 35 U.S.C. 112 Compliance Federal Register Notice (Federal Register, Vol. 84, No. 4, Monday, January 7, 2019, pages 57 to 63). See also http://ptoweb.uspto.gov/patents/exTrain/documents/2019-112-guidance-initiative.pptx . Quoting the FR Notice at pages 61 and 62, "The Federal Circuit emphasized that ‘‘[t]he written description requirement is not met if the specification merely describes a ‘desired result.’ ’’ Vasudevan, 782 F.3d at 682 (quoting Ariad, 598 F.3d at 1349). . . . When examining computer-implemented, software-related claims, examiners should determine whether the specification discloses the computer and the algorithm(s) that achieve the claimed function in sufficient detail that one of ordinary skill in the art can reasonably conclude that the inventor possessed the claimed subject matter at the time of filing. An algorithm is defined, for example, as 'a finite sequence of steps for solving a logical or mathematical problem or performing a task.' Microsoft Computer Dictionary (5th ed., 2002). Applicant may 'express that algorithm in any understandable terms including as a mathematical formula, in prose, or as a flow chart, or in any other manner that provides sufficient structure.' Finisar, 523 F.3d at 1340 (internal citation omitted). It is not enough that one skilled in the art could theoretically write a program to achieve the claimed function, rather the specification itself must explain how the claimed function is achieved to demonstrate that the applicant had possession of it. See, e.g., Vasudevan, 782 F.3d at 682–83. If the specification does not provide a disclosure of the computer and algorithm(s) in sufficient detail to demonstrate to one of ordinary skill in the art that the inventor possessed the invention that achieves the claimed result, a rejection under 35 U.S.C. 112(a) for lack of written description must be made. See MPEP § 2161.01, subsection I."
7 See MPEP 2161.01, I. and LizardTech Inc. v. Earth Resource Mapping Inc., 424 F.3d 1336, 1345 (Fed. Cir. 2005) cited therein ("Whether the flaw in the specification is regarded as a failure to demonstrate that the applicant possessed the full scope of the invention recited in [the claim] or a failure to enable the full breadth of that claim, the specification provides inadequate support for the claim under [§ 112(a)]").
8 For example, published paragraph [0037] of the specification indicates this:
[0037] To test and evaluate hypotheses, a degree of plausibility may be assigned to them. In one simple embodiment, a hypothesis is credited with a previously specified number of points for every confirming sensor measurement. When, in this manner, a certain hypothesis achieves a minimum number of points, it is regarded as plausible. A negative total number of points could result in the hypothesis being rejected. In a further developed embodiment, a probability of being correct is assigned to a certain hypothesis. This requires a probability model that takes into account the correlation between various sensor measurements but also allows complex probability statements to be generated with the aid of stochastic calculation models, thus resulting in a more reliable prediction of the user's expectations. For example, in certain regions (i.e. countries) in which the robot is operated, the width of doors may be standardized. If the robot measures such a standardized width, then this most probably relates to a door. Deviations from the standard widths reduce the probability that they relate to a door. For this purpose, for example, a probability model based on a standard distribution may be used. A further possibility for the generation and evaluation of hypotheses is the use of “machine learning” to generate suitable models and measurement functions (see, e.g. Trevor Hastie, Robert Tibshirani, Jerome Friedman: “The Elements of Statistical Learning”, 2nd edition, Springer Publishing House, 2008). For this purpose, for example, map data is gathered by one or more robots in various living environments. The data can then be supplemented with floor plans or further data input by the user (e.g. regarding the run of doors or doorways or regarding a desired sectoring) and can then be evaluated by a learning algorithm.
9 See MPEP 2161.01, I. and LizardTech Inc. v. Earth Resource Mapping Inc., 424 F.3d 1336, 1345 (Fed. Cir. 2005) cited therein ("Whether the flaw in the specification is regarded as a failure to demonstrate that the applicant possessed the full scope of the invention recited in [the claim] or a failure to enable the full breadth of that claim, the specification provides inadequate support for the claim under [§ 112(a)]").
10 See Nautilus, Inc. v. Biosig Instruments, Inc. (U.S. Supreme Court, 2014) which held, "A patent is invalid for indefiniteness if its claims, read in light of the patent’s specification and prosecution history, fail to inform, with reasonable certainty, those skilled in the art about the scope of the invention." See also In re Packard, 751 F.3d 1307 (Fed.Cir.2014)(“[A] claim is indefinite when it contains words or phrases whose meaning is unclear,” i.e., “ambiguous, vague, incoherent, opaque, or otherwise unclear in describing and defining the claimed invention.”) and Ex Parte McAward, Appeal No. 2015-006416 (PTAB, Aug. 25, 2017, Precedential) (“Applying the broadest reasonable interpretation of a claim, then, the Office establishes a prima facie case of indefiniteness with a rejection explaining how the metes and bounds of a pending claim are not clear because the claim contains words or phrases whose meaning is unclear.”)
11 cor•re•late (v., adj.)
adj., n. v.t.
1. to place in or bring into mutual or reciprocal relation; establish in orderly connection: to correlate expenses and income.
v.i.
2. to have a mutual or reciprocal relation; stand in correlation.
adj.
3. mutually or reciprocally related.
n.
4. either of two related things, esp. when one implies the other.
[From: Random House Kernerman Webster's College Dictionary, © 2010 K Dictionaries Ltd. Copyright 2005, 1997, 1991 by Random House, Inc. All rights reserved. Retrieved 26 February 2026.]
12 See flowchart in MPEP 2111.01, V.
13 robot (ˈrəʊbɒt) n
1. (General Engineering) any automated machine programmed to perform specific mechanical functions in the manner of a human
2. (General Engineering) (modifier) not controlled by man; automatic: a robot pilot.
3. a person who works or behaves like a machine; automaton
. . .
[From: Collins English Dictionary – Complete and Unabridged, 12th Edition 2014 © HarperCollins Publishers 1991, 1994, 1998, 2000, 2003, 2006, 2007, 2009, 2011, 2014. Retrieved 26 February 2026.]
14 See, e.g., RecogniCorp, LLC v. Nintendo Co., 855 F.3d 1322, 1327, 122 USPQ2d 1377 (Fed. Cir. 2017) ("Adding one abstract idea (math) to another abstract idea (encoding and decoding) does not render the claim non-abstract")
15 See e.g., Bilski v. Kappos, 561 U.S. 593 ("Flook established that limiting an abstract idea to one field of use . . . did not make the concept patentable.")